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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
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Classification in the Presence of
Label Noise: a Survey
ıt Fr´
enay and Michel Verleysen, Senior Member, IEEE
Abstract—Label noise is an important issue in classification,
with many potential negative consequences. For example, the
accuracy of predictions may decrease, whereas the complexity of
inferred models and the number of necessary training samples
may increase. Many works in the literature have been devoted to
the study of label noise and the development of techniques to deal
with label noise. However, the field lacks a comprehensive survey
on the different types of label noise, their consequences and the
algorithms that take label noise into account. This paper proposes
to fill this gap. Firstly, the definitions and sources of label noise
are considered and a taxonomy of the types of label noise is
proposed. Secondly, the potential consequences of label noise are
discussed. Thirdly, label noise-robust, label noise cleansing and
label noise-tolerant algorithms are reviewed. For each category
of approaches, a short discussion is proposed in order to help
the practitioner to choose the most suitable technique in its
own particular field of application. Eventually, the design of
experiments is also discussed, what may interest the researchers
who would like to test their own algorithms. In this survey, label
noise consists of mislabelled instances: no additional information
is assumed to be available, like e.g. confidences on labels.
Index Terms—classification, label noise, class noise, misla-
belling, robust methods, survey.
CLASSIFICATION has been widely studied in machine
learning. In that context, the standard approach consists
in learning a classifier from a labelled dataset, in order to
predict the class of new samples. However, real-world datasets
may contain noise, which is defined in [1] as anything that
obscures the relationship between the features of an instance
and its class. In [2], noise is also described as consisting
of non-systematic errors. Among other consequences, many
works have shown that noise can adversely impact the clas-
sification performances of induced classifiers [3]. Hence, the
ubiquity of noise seems to be an important issue for practical
machine learning, e.g. in medical applications where most
medical diagnosis tests are not 100 percent accurate and cannot
be considered a gold standard [4]–[6]. Indeed, classes are
not always as easy to distinguish as lived and died [4]. It is
therefore necessary to implement techniques which eliminate
noise or reduce its consequences. It is all the more necessary
since reliably labelled data are often expensive and time
consuming to obtain [4], what explains the commonness of
noise [7].
In the literature, two types of noise are distinguished: feature
(or attribute) noise and class noise [2], [3], [8]. On the one
The authors are with the ICTEAM institute, Universit´
e catholique de
Louvain, Place du Levant 3, 1348 Louvain-la-Neuve, Belgium. E-mails:
{benoit.frenay, michel.verleysen}
hand, feature noise affects the observed values of the features,
e.g. by adding a small Gaussian noise to each feature during
measurement. On the other hand, class noise alters the ob-
served labels assigned to instances, e.g. by incorrectly setting a
negative label on a positive instance in binary classification. In
[3], [9], it is shown that class noise is potentially more harmful
than feature noise, what highlights the importance of dealing
with this type of noise. The prevalence of the impact of label
noise is explained by the fact 1) that there are many features,
whereas there is only one label and 2) that the importance of
each feature for learning is different, whereas labels always
have a large impact on learning. Similar results are obtained
in [2]: feature noise appears to be less harmful than class noise
for decision trees, except when a large number of features are
polluted by feature noise.
Even if there exists a large literature about class noise,
the field still lacks a comprehensive survey on the different
types of label noise, their consequences and the algorithms
that take label noise into account. This work proposes to cover
the class noise literature. In particular, the different definitions
and consequences of class noise are discussed, as well as the
different families of algorithms which have been proposed to
deal with class noise. As in outlier detection, many techniques
rely on noise detection and removal algorithms, but it is shown
that more complex methods have emerged. Existing datasets
and data generation methods are also discussed, as well as
experimental considerations.
In this work, class noise refers to observed labels which are
incorrect. It is assumed that no other information is available,
contrarily to other contexts where experts can e.g. provide a
measure of confidence or uncertainty on their own labelling or
answer with sets of labels. It is important to make clear that
only the observed label of an instance is affected, not its true
class. For this reason, class noise is called here label noise.
The survey is organised as follows. Section II discusses
several definitions and sources of label noise, as well as a
new taxonomy inspired by [10]. The potential consequences of
label noise are depicted in Section III. Section IV distinguishes
three types of approaches to deal with label noise: label noise-
robust methods, label noise cleansing methods and label noise-
tolerant methods. The three families of methods are discussed
in Sections V, VI and VII, respectively. Section VIII discusses
the design of experiments in the context of label noise and
Section IX concludes the survey.
Label noise is a complex phenomenon, as shown in this
section. First, Section II-A defines label noise and specifies the
scope of the survey. Similarities and differences with outliers
and anomalies are also highlighted, since outlier detection
methods can be used to detect mislabelled instances. Next,
Section II-B reviews various sources of label noise, including
insufficient information, expert labelling errors, subjectivity
of the classes and encoding and communication problems.
Eventually, a taxonomy of the types of label noise is proposed
in Section II-C in order to facilitate further discussions.
The proposed taxonomy highlights the potentially complex
relationships between the features of instances, their true class
and their observed label. This complexity should be taken into
account when designing algorithms to deal with label noise,
as they should be adapted to the characteristics of label noise.
A. Definition of Label Noise and Scope of the Survey
Classification consists in predicting the class of new sam-
ples, using a model inferred from training data. In this survey,
it is assumed that each training sample is associated with an
observed label. This label often corresponds to the true class of
the sample, but it may be subjected to a noise process before
being presented to the learning algorithm [11]. It is therefore
important to distinguish the true class of an instance from its
observed label. The process which pollutes labels is called
label noise and must be separated from feature (or attribute)
noise [2], [3], [8] which affects the value of features. Some
authors also consider outliers which are correctly labelled as
label noise [12], what is not done here.
In this survey, label noise is considered to be a stochastic
process, i.e. the case where the labelling errors may be
intentionally (like e.g. in the food industry [13]–[16]) and
maliciously induced by an adversary agent [17]–[26] is not
considered. Moreover, labelling errors are assumed to be inde-
pendent from each other [11]. Edmonds [27] shows that noise
in general is a complex phenomenon. In some very specific
contexts, stochastic label noise can be intentionally introduced
e.g. to protect people privacy, in which case its characteristics
are completely under control [28]. However, a fully specified
model of label noise is usually not available, what explains
the need for automated algorithms which are able to cope
with label noise. Learning situations where label noise occurs
can be called imperfectly supervised, i.e. pattern recognition
applications where the assumption of label correctness does
not hold for all the elements of the training sample [29]. Such
situations are between supervised and unsupervised learning.
Dealing with label noise is closely related to outlier de-
tection [30]–[33] and anomaly detection [34]–[38]. Indeed,
mislabelled instances may be outliers, if their label has a
low probability of occurrence in their vicinity. Similarly, such
instances may also look anomalous, with respect to the class
which corresponds to their incorrect label. Hence, it is natural
that many techniques in the label noise literature are very close
to outlier and anomaly detection techniques; this is detailed
in Section VI. In fact, many of the methods which have been
developed to deal with outliers and anomalies can also be used
to deal with label noise (see e.g. [39], [40]). However, it must
be highlighted that mislabelled instances are not necessarily
outliers or anomalies, which are subjective concepts [41]. For
example, if labelling errors occur in a boundary region where
all classes are equiprobable, the mislabelled instances neither
are rare events nor look anomalous. Similarly, an outlier is not
necessarily a mislabelled sample [42], since it can be due to
feature noise or simply be a low-probability event.
B. Sources of Label Noise
As outlined in [1], the identification of the source(s) of
label noise is not necessarily important, when the focus of
the analysis is on the consequences of label noise. However,
when a label noise model has to be embedded directly into the
learning algorithm, it may be important to choose a modelling
which accurately explains the actual label noise.
Label noise naturally occurs when human experts are in-
volved [43]. In that case, possible causes of label noise include
imperfect evidence, patterns which may be confused with
the patterns of interest, perceptual errors or even biological
artefacts. See e.g. [44], [45] for a philosophical account
on probability, imprecision and uncertainty. More generally,
potential sources of label noise include four main classes.
Firstly, the information which is provided to the expert
may be insufficient to perform reliable labelling [1], [46].
For example, the results of several tests may be unknown in
medical applications [12]. Moreover, the description language
may be too limited [47], what reduces the amount of available
information. In some cases, the information is also of poor or
variable quality. For example, the answers of a patient during
anamnesis may be imprecise or incorrect or even may be
different if the question is repeated [48].
Secondly, as mentioned above, errors can occur in the expert
labelling itself [1]. Such classification errors are not always
due to human experts, since automated classification devices
are used nowadays in different applications [12]. Also, since
collecting reliable labels is a time-consuming and costly task,
there is an increasing interest in using cheap, easy-to-get
labels from non-expert using frameworks like e.g. the Amazon
Mechanical Turk1[49]–[52]. Labels provided by non-expert
are less reliable, but Snow et al. [49] show that the wealth of
available labels may alleviate this problem.
Thirdly, when the labelling task is subjective, like e.g. in
medical applications [53] or image data analysis [54], [55],
there may exist an important variability in the labelling by
several experts. For example, in electrocardiogram analysis,
experts seldom agree on the exact boundaries of signal patterns
[56]. The problem of inter-expert variability was also noticed
during the labelling of the Penn Treebank, an annotated corpus
of over 4.5 million words [57].
Eventually, label noise can also simply come from data
encoding or communication problems [3], [11], [46]. For
example, in spam filtering, sources of label noise include mis-
understanding the feedback mechanisms and accidental click
[58]. Real-world databases are estimated to contain around five
percents of encoding errors, all fields taken together, when no
specific measures are taken [59]–[61].
C. Taxonomy of Label Noise
In the context of missing values, Schafer and Graham
[10] discuss a taxonomy which is adapted below to provide
a new taxonomy for label noise. Similarly, Nettleton et al.
[62] characterise noise generation in terms of its distribution,
the target of the noise (features, label, etc.) and whether its
magnitude depends on the data value of each variable. Since
it is natural to consider label noise from a statistical point of
view, Fig. 1 shows three possible statistical models of label
noise. In order to model the label noise process, four random
variables are depicted: Xis the vector of features, Yis the
true class, ˜
Yis the observed label and Eis a binary variable
telling whether a labelling error occurred (Y6=˜
Y). The set of
possible feature values is X, whereas the set of possible classes
(and labels) is Y. Arrows report statistical dependencies: for
example, ˜
Yis assumed to always depend on Y(otherwise,
there is no sense in using the labels).
1) The Noisy Completely at Random Model: In Fig. 1(a),
the relationship between Yand ˜
Yis called noisy completely at
random (NCAR): the occurrence of an error Eis independent
of the other random variables, including the true class itself.
In the NCAR case, the observed label is different from the
true class with a probability pe=P(E= 1) = P(Y6=˜
[11], sometime called the error rate or the noise rate [63]. In
the case of binary classification, NCAR noise is necessarily
symmetric: the same percentage of instances are mislabelled
in both classes. When pe=1
2, the labels are useless, since
they no longer carry any information [11]. The NCAR setting
is similar to the absent-minded professor discussed in [64].
In the case of multiclass classification, it is usually assumed
that the incorrect label is chosen at random in Y \ {y}when
E= 1 [11], [65]. In other words, a biased coin is firstly
flipped in order to decide whether the observed label is correct
or not. If the label is wrong, a fair dice with |Y| − 1faces
(where |Y| is the number of classes) is tossed to choose the
observed, wrong label. This particularly simple model is called
the uniform label noise.
2) The Noisy at Random Model: In Fig. 1(b), it is assumed
that the probability of error depends on the true class Y, what
is called here noisy at random (NAR). Eis still independent of
X, but this model allows modelling asymmetric label noise,
i.e. when instances from certain classes are more prone to
be mislabelled. For example, in medical case-control studies,
control subjects may be more likely to be mislabelled. Indeed,
the test which is used to label case subjects may be too
invasive (e.g. a biopsy) or too expensive to be used on control
subjects and is therefore replaced by a suboptimal diagnostic
test for control subjects [66]. Since one can define the labelling
Y= ˜y|Y=y) =
Y= ˜y|E=e, Y =y)P(E=e|Y=y),(1)
the NAR label noise can equivalently be characterised in terms
of the labelling (or transition) matrix [67], [68]
γ11 · · · γ1nY
γnY1· · · γnYnY
Y= 1|Y= 1) · · · P(˜
Y=nY|Y= 1)
Y= 1|Y=nY)· · · P(˜
where nY=|Y| is the number of classes. Each row of the
labelling matrix must sum to 1, since P˜y∈Y P(˜
Y= ˜y|Y=
y) = 1. For example, the uniform label noise corresponds to
the labelling matrix
1pe· · · pe
nY1· · · 1pe
Notice that NCAR label noise is a special case of NAR label
noise. When true classes are known, the labelling probabilities
can be directly estimated by the frequencies of mislabelling in
data, but it is seldom the case [48]. Alternately, one can also
use the incidence-of-error matrix [48]
π1γ11 · · · π1γ1nY
πnYγnY1· · · πnYγnYnY
P(Y= 1,˜
Y= 1) · · · P(Y= 1,˜
Y= 1) · · · P(Y=nY,˜
where πy=P(Y=y)is the prior of class y. The entries of
the incidence-of-error matrix sum to one and may be of more
practical interest.
With the exception of uniform label noise, NAR label noise
is the most commonly studied case of label noise in the
literature. For example, Lawrence and Sch¨
olkopf [67] consider
arbitrary labelling matrices. In [3], [69], pairwise label noise
is introduced: 1) two classes c1and c2are selected, then 2)
each instance of class c1has a probability to be incorrectly
labelled as c2and vice versa. For this label noise, only two
non-diagonal entries of the labelling matrix are non-zero.
In the case of NAR label noise, it is no longer trivial to
decide whether the labels are helpful or not. One solution is
to compute the expected probability of error
pe=P(E= 1) = X
P(Y=y)P(E= 1|Y=y)(5)
and to require that pe<1
2, similarly to NCAR label noise.
However, this condition does not prevent the occurrence of
very small correct labelling probabilities P(˜
some class y∈ Y, in particular if the prior probability P(y)
of this class is small. Instead, conditional error probabilities
pe(y) = P(E= 1|Y=y)can also be used.
(a) (b) (c)
Fig. 1. Statistical taxonomy of label noise inspired by [10]: (a) noisy completely at random (NCAR), (b) noisy at random (NAR) and (c) noisy not at random
(NNAR). Arrows report statistical dependencies. Notice the increasing complexity of statistical dependencies in the label noise generation models, from left
to right. The statistical link between Xand Yis not shown for clarity.
3) The Noisy not at Random Model: Most works on label
noise consider that the label noise affects all instances with
no distinction. However, it is not always realistic to assume
the two above types of label noise [11], [70]. For example,
samples may be more likely mislabelled when they are similar
to instances of another class [70]–[76], as illustrated e.g. in
[77] where empirical evidence is given that more difficult
samples in a text entailment dataset are labelled randomly.
It also seems natural to expect less reliable labels in regions
of low density [78]–[80], where experts predictions may be
actually based on a very small number of similar previously
encountered cases.
Let us consider a more complex and realistic model of
label noise. In Fig. 1(c), Edepends on both variables X
and Y, i.e. mislabelling is more probable for certain classes
and in certain regions of the Xspace. This noisy not at
random (NNAR) model is the most general case of label noise
[81], [82]. For example, mislabelling near the classification
boundary or in low density regions can only be modelled
in terms of NNAR label noise. Such a situation occurs e.g.
in speech recognition, where automatic speech recognition
is more difficult in case of phonetic similarity between the
correct word and the recognised word [83]. The context of
each word can be considered in order to detect incorrect
recognitions. Notice that the medical literature distinguishes
differential (feature-dependent, i.e. NNAR) label noise and
non-differential (feature-independent, i.e. NCAR or NAR)
label noise [84].
The reliability of labels is even more complex to estimate
that for NCAR or NAR label noise. Indeed, the probability of
error also depends in that case on the value of X. As before,
one can define an expected probability of error which becomes
pe=P(E= 1) = X
P(X=x|Y=y)P(E= 1|X=x, Y =y)dx (6)
if Xis continuous. However, this quantity does not reflect the
local nature of label noise: in some cases, pecan be almost
zero although the density of labelling errors shows important
peaks in certain regions. The quantity pe(x, y) = P(E=
1|X=x, Y =y)may therefore be more appropriate to
characterise the reliability of labels.
In this section, the potential consequences of label noise
are described to show the necessity to take label noise into
account in learning problems. Section III-A reviews theoretical
and empirical evidences of the impact of label noise on clas-
sification performances, which is the most frequently reported
issue. Section III-B shows that the presence of label noise also
increases the necessary number of samples for learning, as
well as the complexity of models. Label noise may also pose
a threat for related tasks, like e.g. class frequencies estimation
and feature selection, which are discussed in Section III-C and
Section III-D, respectively.
This section presents the negative consequences of label
noise, but artificial label noise also has potential advantages.
For example, label noise can be added in statistical studies to
protect people privacy: it is e.g. used in [28] to obtain statistics
for questionnaires, while making impossible to recover indi-
vidual answers. In [85]–[89], label noise is added to improve
classifier results. Whereas bagging produces different training
sets by resampling, these works copy the original training set
and switch labels in new training sets to increase the variability
in data.
A. Deterioration of Classification Performances
The more frequently reported consequence of label noise
is a decrease in classification performances, as shown in the
theoretical and experimental works described below.
1) Theoretical Studies of Simple Classifiers: There exist
several theoretical studies of the consequences of label noise
on prediction performances. For simple problems and sym-
metric label noise, the accuracy of classifiers may remain
unaffected. Lachenbruch [71] consider e.g. the case of binary
classification when both classes have Gaussian distribution
with identical covariance matrix. In such a case, a linear
discriminant function can be used. For a large number of
samples, the consequence of uniform noise is noticeable only
if the error rates α1and α2in each class are different. In
fact, the change in decision boundary is completely described
in terms of the difference α1α2. These results are also
discussed asymptotically in [90].
The results of Lachenbruch [71] are extended in [91] for
quadratic discriminant functions, i.e. Gaussian conditional
distributions with unequal covariance matrices. In that case,
prediction is affected even when label noise is symmetric
among classes (α1=α2). Consequences worsen when
differences in covariance matrices or misclassification rates
increase. Michalek and Tripathi [92] and Bi and Jeske [93]
show that label noise affects normal discriminant and logistic
regression: their error rates are increased and their parameters
are biased. Logistic regression seems to be less affected.
In [64], the single-unit perceptron is studied in the presence
of label noise. If the teacher providing learning samples is
absent-minded, i.e. labels are flipped with a given probability
(uniform noise), the performances of a learner who takes the
labels for granted are damaged and even get worse than the
performances of the teacher.
Classification performances of the knearest neighbours
(kNN) classifier are also affected by label noise [94], [95],
in particular when k= 1 [96]. Okamoto and Nobuhiro [96]
present an average-case analysis of the kNN classifier. When k
is optimised, the consequences of label noise are reduced and
remain small unless a large amount of label noise is added.
The optimal value of kdepends on both the number of training
instances and the presence of label noise. For small noise-free
training sets, 1NN classifiers are often optimal. But as soon as
label noise is added, the optimal number of neighbours kis
shown to monotonically increase with the number of instances
even for small training sets, what seems natural since 1NN
classifiers are particularly affected by label noise.
2) Experimental Assessment of Specific Models: Apart
from theoretical studies, many works show experimentally
that label noise may be harmful. First of all, the impact of
label noise is not identical for all types of classifiers. As
detailed in Section V, this fact can be used to cope (at least
partially) with label noise. For example, Nettleton et al. [62]
compare the impact of label noise on four different supervised
learners: naive Bayes, decision trees induced by C4.5, kNNs
and support vector machines (SVMs). In particular, naive
Bayes achieves the best results, what is attributed to the con-
ditional independence assumption and the use of conditional
probabilities. This should be contrasted with the results in [12],
where naive Bayes is sometime dominated by C4.5 and kNNs.
The poor results of SVMs are attributed to its reliance on
support vectors and the feature interdependence assumption.
In text categorization, Zhang and Yang [97] consider the
robustness of regularized linear classification methods. Three
linear methods are tested by randomly picking and flipping
labels: linear SVMs, Ridge regression and logistic regression.
The experiments show that the results are dramatically affected
by label noise for all three methods, which obtain almost iden-
tical performances. Only 5% of flipped labels already leads to
a dramatic decrease of performances, what is explained by
the presence of relatively very small classes with only a few
samples in their experiments.
Several studies have shown that boosting [98] is affected by
label noise [99]–[102]. In particular, the adaptive boosting al-
gorithm AdaBoost tends to spend too much efforts on learning
mislabelled instances [100]. During learning, successive weak
learners are trained and the weights of instances which are
misclassified at one step are increased at the next step. Hence,
in the late stages of learning, AdaBoost tends to increase the
weights of mislabelled instances and starts overfitting [103],
[104]. Dietterich [100] clearly shows that the mean weight
per training sample becomes larger for mislabelled samples
than for correctly labelled samples as learning goes on. In-
terestingly, it has been shown in [105]–[108] that AdaBoost
tends to increase the margins of the training examples [109]
and achieves asymptotically a decision with hard margin, very
similar to the one of SVMs for the separable case [108]. This
may not be a good idea in the presence label noise and may
explain why AdaBoost overfits noisy training instances. In
[110], it is also shown that ensemble methods can fail simply
because the presence of label noise affects the ensembled
models. Indeed, learning through multiple models becomes
harder for large levels of label noise, where some samples
become more difficult for all models and are therefore seldom
correctly classified by an individual model.
In systems which learn Boolean concepts with disjuncts,
Weiss [111] explains that small disjuncts (which individually
cover only a few examples) are more likely to be affected
by label noise than large disjuncts covering more instances.
However, only large levels of label noise may actually be a
problem. For decision trees, it appears in [2] that destroying
class information produces a linear increase in error. Taking
logic to extremes, when all class information is noise, the
resulting decision tree classifies objects entirely randomly.
Another example studied in [58] is spam filtering where
performances are decreased by label noise. Spam filters tend
to overfit label noise, due to aggressive online update rules
which are designed to quickly adapt to new spam.
3) Additional Results for More Complex Types of Label
Noise: The above works deal with NAR label noise, but
more complex types of label noise have been studied in the
literature. For example, in the case of linear discriminant
analysis (LDA), i.e. binary classification with normal class
distributions, Lachenbruch [70] considers that mislabelling
systematically occurs when samples are too far from the
mean of their true class. In that NNAR label noise model,
the true probabilities of misclassification are only slightly
affected, whereas the populations are better separated. This is
attributed to the reduction of the effects of outliers. However,
the apparent error rate [112] of LDA is highly influenced, what
may cause the classifier to overestimate its own efficiency.
LDA is also studied in the presence of label noise by
[72], which generalises the results of [70], [71], [90]–[92].
Let us define 1) the misallocation rate αyfor class y, i.e.
the number of samples with label ywhich belong to the
other class and 2) a z-axis which passes through the center
of both classes and is oriented towards the positive class,
such that each center is located at z=±
2. In [72], three
label noise models are defined and characterised in terms of
the probability of misallocation gy(z), which is a monotone
decreasing (increasing) function of zfor positive (negative)
samples. In random misallocation, gy(z) = αyis constant
for each class, what is equivalent to the NAR label noise.
In truncated label noise, g(z)is zero as long as the instance
is close enough to the mean of its class. Afterwards, the
mislabelling probability is equal to a small constant. This
type of NNAR label noise is equivalent to the model of
[70] when the constant is equal to one. Eventually, in the
exponential model, the probability of misallocation becomes
for the negative class
gy(z) = (0if z≤ −
1exp 1
2)2if z >
where is the distance between the centres of both classes
and ky= (1 2αy)2. A similar definition is given for the
positive class. For equivalent misallocation rates αy, random
misallocation has more consequences than truncated label
noise, in terms of influence on the position and variability of
the discriminant boundary. In turn, truncated label noise itself
has more consequences than exponential label noise. The same
ordering appears when comparing misclassification rates.
B. Consequences on Learning Requirements and Model Com-
Label noise can affect learning requirements (e.g. number
of necessary instances) or the complexity of learned models.
For example, Quinlan [2] warns that the size of decision trees
may increase in case of label noise, making them overly com-
plicated, what is confirmed experimentally in [46]. Similarly,
an and Masegosa [104] show that the number of nodes
of decision trees induced by C4.5 for bagging is increased,
while the resulting accuracy is reduced. Reciprocally, Brodley
and Friedl [46] and Libralon et al. [113] show that removing
mislabelled samples reduces the complexity of SVMs (number
of support vectors), decision trees induced by C4.5 (size of
trees) and rule-based classifiers induced by RIPPER (number
of rules). Post-pruning also seems to reduce the consequences
of label noise [104]. Noise reduction can therefore produce
models which are easier to understand, what is desirable in
many circumstances [114]–[116]
In [11], it is shown that the presence of uniform label noise
in the probably approximately correct (PAC) framework [117]
increases the number of necessary samples for PAC identifica-
tion. An upper bound for the number of necessary samples is
given, which is strengthened in [118]. Similar bounds are also
discussed in [65], [119]. Also, Angluin and Laird [11] discuss
the feasibility of PAC learning in the presence of label noise
for propositional formulas in conjunctive normal form (CNF),
what is extended in [120] for Boolean functions represented
by decision trees and in [73], [121] for linear perceptrons.
C. Distortion of Observed Frequencies
In medical applications, it is often necessary to perform
medical tests for disease diagnosis, to estimate the preva-
lence of a disease in a population or to compare (estimated)
prevalence in different populations. However, label noise can
affect the observed frequencies of medical test results, what
may lead to incorrect conclusions. For binary tests, Bross
[4] shows that mislabelling may pose a serious threat: the
observed mean and variance of the test answer is strongly
affected by label noise. Let us consider a simple example
taken from [4]: if the minority class represents 10% of the
dataset and 5% of the test answers are incorrect (i.e. patients
are mislabelled), the observed proportion of minority cases is
0.95×10%+0.05×90% = 14% and is therefore overestimated
by 40%. Significance tests which assess the difference between
the proportions of both classes in two populations are still
valid in case of mislabelling, but their power may be strongly
reduced. Similar problems occur e.g. in consumer survey
analysis [122].
Frequency estimates are also affected by label noise in
multiclass problems. Hout and Heijden [28] discuss the case
of artificial label noise, which can be intentionally introduced
after data collection in order to preserve privacy. Since the
label noise is fully specified in this case, it is possible to
adjust the observed frequencies. When a model of the label
noise is not available, Tenenbein [123] proposes to solve the
problem pointed by [4] using double sampling, which uses
two labellers: an expensive, reliable labeller and a cheap,
unreliable labeller. The model of mislabelling can thereafter
be learned from both sets of labels [124], [125]. In [48], the
case of multiple experts is discussed in the context of medical
anamnesis; an algorithm is proposed to estimate the error rates
of the experts.
Evaluating the error rate of classifiers is also important for
both model selection and model assessment. In that context,
Lam and Stork [126] show that label noise can have an im-
portant impact on the estimated error rate, when test samples
are also polluted. Hence, mislabelling can also bias model
comparison. As an example, a spam filter with a true error
rate of 0.5%, for example, might be estimated to have an error
rate between 5.5% and 6.5% when evaluated using labels with
an error rate of 6.0%, depending on the correlation between
filter and label errors [127].
D. Consequences for Related Tasks
The aforementioned consequences are not the only possible
consequences of label noise. For example, Zhang et al. [128]
show that the consequences of label noise are important
in feature selection for microarray data. In an experiment,
only one mislabelled sample already leads to about 20% of
not identified discriminative genes. Notice that in microarray
data, only a few data are available. Similarly, Shanab et al.
[129] show that label noise decreases the stability of feature
rankings. The sensitivity of feature selection to label noise is
also illustrated for logistic regression in [130]. A methodology
to achieve feature selection for classification problems polluted
by label noise is proposed in [131], based on a probabilistic
label noise model combined with a nearest neighbours-based
estimator of the mutual information.
E. Conclusion
This section shows that the consequences of label noise are
important and diverse: decrease in classification performances,
changes in learning requirements, increase in the complexity
of learned models, distortion of observed frequencies, diffi-
culties to identify relevant features, etc. The nature and the
importance of the consequences depend, among others, on the
type and the level of label noise, the learning algorithm and the
characteristics of the training set. Hence, it seems important
for the machine learning practitioner to deal with label noise
and to consider these factors, prior to the analysis of polluted
In light of the various consequences detailed in Section III,
it seems important to deal with label noise. In the literature,
there exist three main approaches to take care of label noise
[12], [82], [132]–[137]; these approaches are described below.
Manual review of training samples is not considered in this
survey, because it is usually prohibitively costly and time
consuming, if not impossible in the case of large datasets.
A first approach relies on algorithms which are naturally
robust to label noise. In other words, the learning of the
classifier is assumed to be not too sensitive to the presence
of label noise. Indeed, several studies have shown that some
algorithms are less influenced than others by label noise, what
advocates for this approach. However, label noise is not really
taken into account in this type of approach. In fact, label noise
handling is entrusted to overfitting avoidance [132]–[134].
Secondly, one can try to improve the quality of training
data using filter approaches. In such a case, noisy labels
are typically identified and being dealt with before training
occurs. Mislabelled instances can either be relabelled or simply
removed [138]. Filter approaches are cheap and easy to
implement, but some of them are likely to remove a substantial
amount of data.
Eventually, there exist algorithms which directly model
label noise during learning or which have been modified to
take label noise into account in an embedded fashion. The ad-
vantage of this approach is to separate the classification model
and the label noise model, what allows using information about
the nature of label noise.
The literature for the three above trends of approaches is
reviewed in the three next sections. In some cases, it is not
always clear whether an approach belongs to one category
or the other. For example, some of the label noise-tolerant
variants of SVMs could also be seen as filtering. Table I gives
an overview of the main methods considered in this survey. At
the end of each section, a short discussion of the strengths and
weaknesses of the described techniques is proposed, in order to
help the practitioner in its choice. The three following sections
are strongly linked with Section III. Indeed, the knowledge of
the consequences of label noise allows one to avoid some
pitfalls and to design algorithms which are more robust or
tolerant to label noise. Moreover, the consequences of label
noise themselves can be used to detect mislabelled instances.
This section describes models which are robust to the pres-
ence of label noise. Even if label noise is neither cleansed nor
modelled, such models have been shown to remain relatively
effective when training data are corrupted by small amounts
of label noise. Label noise-robustness is discussed from a
theoretical point of view in Section V-A. Then, the robustness
of ensembles methods and decision trees are considered in
Section V-B and V-C, respectively. Eventually, various other
methods are discussed in Section V-D and Section V-E con-
cludes about the practical use of label noise-robust methods.
A. Theoretical Considerations on the Robustness of Losses
Before we turn to empirical results, a first, fundamental
question is whether it is theoretically possible (and under what
circumstances) to achieve perfect label noise-robustness. In or-
der to have a general view of label noise-robustness, Manwani
and Sastry [82] study learning algorithms in the empirical risk
minimisation (ERM) framework for binary classification. In
ERM, the cost of wrong predictions is measured by a loss
and classifiers are learned by minimising the expected loss
for future samples, which is called the risk. The more natural
loss is the 0-1 loss, which gives a cost of 1in case of error
and is zero otherwise. However, the 0-1 loss is neither convex
nor differentiable, what makes it intractable for real learning
algorithms. Hence, others losses are often used in practice,
which approximate the 0-1 loss by a convex function, called
a surrogate [139].
In [82], risk minimisation under a given loss function is
defined as label noise-robust if the probability of misclassifica-
tion of inferred models is identical, irrespective of label noise
presence. It is demonstrated that the 0-1 loss is label noise-
robust for uniform label noise [140] or when it is possible to
achieve zero error rate [81]; see e.g. [74] for a discussion in the
case of NNAR label noise. The least-square loss is also robust
to uniform label noise, which guarantees the robustness of the
Fisher linear discriminant in that specific case. Other well-
known losses are shown to be not robust to label noise, even
in the uniform label noise case: 1) the exponential loss, which
leads to AdaBoost, 2) the log loss, which leads to logistic
regression and 3) the hinge loss, which leads to support vector
machines. In other words, one can expect most of the recent
learning algorithms in machine learning to be not completely
label noise-robust.
B. Ensemble Methods: Bagging and Boosting
In the presence of label noise, bagging achieves better
results than boosting [100]. On the one hand, mislabelled
instances are characterised by large weights in AdaBoost,
which spends too much effort in modelling noisy instances
[104]. On the other hand, mislabelled samples increase the
variability of the base classifiers for bagging. Indeed, since
each mislabelled sample has a large impact on the classifier
and bagging repeatedly selects different subsets of training
instances, each resampling leads to a quite different model.
Hence, the diversity of base classifiers is improved in bag-
ging, whereas the accuracy of base classifiers in AdaBoost is
severely reduced.
Several algorithms have been shown to be more label noise-
robust than AdaBoost [101], [102], e.g. LogitBoost [141] and
BrownBoost [142]. In [108], [143]–[145], boosting is casted
as a margin maximisation problem and slack variables are
introduced in order to allow a given fraction of patterns to
stand in the margin area. Similarly to soft-margin SVMs, these
works propose to allow boosting to misclassify some of the
training samples, what is not directly aimed at dealing with
Section V: label noise-robust methods Section VII.A: probabilistic label noise-tolerant methods
A robust losses for classification [74], [81], [82], [140]
B ensemble methods like LogitBoost [141], BrownBoost [142] and
boosting with margin maximisation [108], [143]–[145]
C split criteria for trees like the imprecise info-gain [104], [148]–[150]
A.1 Bayesian approaches [68] including e.g 1) priors on the mislabelling
probabilities [5], [122], [227], [228] like Beta priors [5], [128], [229],
[230], [232]–[236] and Dirichlet priors [237], [238], 2) Bayesian
methods for logistic regression [130], [236], [239]–[241], hidden
Markov models [84] and graphical models [242] and 3) procedures
based on mislabelling indicator variables [128], [235], [245], [246]
A.2 frequentist approaches including e.g. mixture models [249], [250] and
label noise model-based methods [66], [67], [251]–[256]
A.3 clustering methods assigning clusters to classes [136], [262],[263]
A.4 belief function-based methods that directly infer belief masses from
data [78], [80], [271] to account for the uncertainty on labels
Section VI: data cleansing methods Section VII.B model-based label noise-tolerant methods
A detection of mislabelled instances with measures like the
classification confidence [157] and the model complexity [158]–[162]
B model predictions-based filtering, i.e. 1) classification filtering that
remove misclassified training instances [165]–[167] with e.g. local
models [115], [116], [174], [178], 2) voting filtering [46], [138],
[161], [173], [180], [182]–[184] and 3) partition filtering [69], [185]
C model influence [53], [187], [188] and introspection [64]
D k nearest neighbours-based methods [95], [193], including e.g. CNN
[195], RNN [196], BBNR [197], DROP1-6 [95], [193], GE [29],
[200], IB3 [204], [205], Tomek links [206], [207] and PRISM [208]
E neighbourhood graph-based methods [94], [209], [212]–[214]
F ensemble-based methods with removal of e.g. instances with highest
weights [184], [215] and often misclassified instances [217]
B.1 embedded data cleansing for SVMs [273]–[277] and robust losses
[280], [285] to produce label noise-tolerant SVMs without filtering
B.2 label noise-tolerant variants of the perceptron algorithm [286] like
the λ-trick [287], [288], the α-bound [289] and PAM [286], [290]
B.3 decision trees with a good trade-off between accuracy and simplicity
obtained using e.g. the CN2 algorithm [291]
B.4 boosting methods that 1) carefully update weights like MadaBoost
[292], AveBoost [293] and AveBoost2 [294], 2) combine bagging and
boosting like BB [297] and MB [298] and 3) distinguish safe, noisy
and borderline patterns like reverse boosting [299]
B.5 semi-supervised methods that 1) prevent mislabelled instances to
influence the label of unlabelled instances [7], 2) detect mislabelled
instances using unlabelled instances [300]–[302] and 3) deal with
mistakes done when labelling unlabelled samples like in [304]–[306]
or in the case of co-training [307]–[309]
label noise but robustifies boosting. Moreover, this approach
can be used to find difficult or informative patterns [145].
C. Decision trees
It is well-known that decision trees are greatly impacted by
label noise [2], [104]. In fact, their instability makes them well
suited for ensemble methods [146]–[148]. In [148], different
node split criteria are compared for ensembles of decision trees
in the presence of label noise. The imprecise info-gain [149]
is shown to improve accuracy, with respect to the information
gain, the information gain ratio and the Gini index. Compared
to ensembles of decision trees inferred by C4.5, Abell´
an and
Masegosa [104] also show that the imprecise info-gain allows
reducing the size of the decision trees. Eventually, they observe
that post-pruning of decision trees can reduce the impact of
label noise. The approach is extended for continuous features
and missing data in [150].
D. Other Methods
Most of the studies on label noise robustness have been
presented in Section III. They show that complete label noise
robustness is seldom achieved, as discussed in Section V-A. An
exception is [81], where the 0-1 loss is directly optimised using
a team of continuous-action learning automata: 1) a probability
distribution is defined on the weights of a linear classifier,
then 2) weights are repetitively drawn from the distribution to
classify training samples and 3) the 0-1 losses for the training
samples are used at each iteration as a reinforcement to pro-
gressively tighten the distribution around the optimal weights.
In the case of separable classes, the approach converges to
the true optimal separating hyperplane, even in the case of
NNAR label noise. In [151], eleven classifiers are compared
on imbalanced datasets with asymmetric label noise. In all
cases, the performances of the models are affected by label
noise. Random forests [147] are shown to be the most robust
among the eleven methods, what is also the case in another
study by the same authors [152]. C4.5, radial basis function
(RBF) networks and rule-based classifiers obtain the worst
results. The sensitivity of C4.5 to label noise is confirmed
in [153], where multilayer perceptrons are shown to be less
affected. In [135], a new artificial immune recognition system
(AIRS) is proposed, called RWTSAIRS, which is shown to be
less sensitive to label noise. In [154], two procedures based
on argumentation theory are also shown to be robust to label
noise. In [12], it is shown that feature extraction can help
to reduce the impact of label noise. Also, S`
aez et al. [9],
[155] shows that using one-vs-one decomposition in multiclass
problems can improve the robustness, which could be due
to the distribution of the noisy examples in the subproblems,
the increase of the separability of the classes and collecting
information from different classifiers.
E. Discussion
Theoretically, common losses in machine learning are not
completely robust to label noise [139]. However, overfitting
avoidance techniques like e.g. regularisation can be used to
partially handle label noise [132]–[134], even if label noise
may interfere with the quality of the classifier, whose accuracy
might suffer and the representation might be less compact
[132]. Experiments in the literature show that the performances
of classifiers inferred by label noise-robust algorithms are still
affected by label noise. Label noise-robust methods seem to
be adequate only for simple cases of label noise, which can
be safely managed by overfitting avoidance.
When training data is polluted by label noise, an obvious
and tempting solution consists in cleansing the training data
themselves, what is similar to outlier or anomaly detection.
However, detecting mislabelled instances is seldom trivial:
Weiss and Hirsh [156] show e.g. in the context of learning
with disjuncts that true exceptions may be hard to distinguish
from mislabelled instances. Hence, many methods have been
proposed to cleanse training sets, with different degrees of
success. The whole procedure is illustrated by Fig. 2, which
is inspired by [46]. This section describes several methods
which detect, remove or relabel mislabelled instances. First,
simple methods based on thresholds are presented in Section
VI-A. Model prediction-based filtering methods are discussed
in Section VI-B, which includes classification filtering, voting
filtering and partition filtering. Methods based on measures
of the impact of label noise and introspection are considered
in Section VI-C. Sections VI-D, VI-E and VI-F address
methods based on nearest neighbours, graphs and ensembles.
Eventually, several other methods are discussed in Section
VI-G and a general discussion about data cleansing methods
is proposed in Section VI-H.
A. Measures and Thresholds
Similarly to outlier detection [30]–[33] and anomaly detec-
tion [34]–[38], several methods in label noise cleansing are
based on ad hoc measures. Instances can e.g. be removed
when the anomaly measure exceeds a predefined threshold. For
example, in [157], the entropy of the conditional distribution
P(Y|X)is estimated using a probabilistic classifier. Instances
with a low entropy correspond to confident classifications.
Hence, such instances for which the classifier disagrees with
the observed label are relabelled using the predicted label.
As discussed in Section III, label noise may increase the
complexity of inferred models. Therefore, complexity mea-
sures can be used to detect mislabelled instances, which
disproportionately increase model complexity when added to
the training set. In [158], the complexity measure for inductive
concept learning is the number of literals in the hypothesis. A
cleansing algorithm is proposed, which 1) finds for each literal
the minimal set of training samples whose removal would
allow going without the literal and 2) awards one point to
each sample in the minimal set. Once all literals have been
reviewed, the sample with the higher score is removed, if the
score is high enough. This heuristic produces less complex
models. Similarly, Gamberger and Lavraˇ
c [159] measure the
complexity of the least complex correct hypothesis (LCCH)
for a given training set. Each training set is characterised by
a LCCH value and is saturated if its LCCH value is equal to
the complexity of the target hypothesis. Mislabelled samples
are removed to obtain a saturated training set. Gamberger et
al. [160]–[162] elaborate on the above notions of complexity
and saturation, which result in the so-called saturation filter.
B. Model Predictions-Based Filtering
Several data cleansing algorithms rely on the predictions of
classifiers: classification filtering, voting filtering and partition
filtering. In [163], such methods are extended in the context
of cost-sensitive learning, whereas Khoshgoftaar and Rebours
[164] propose a generic algorithm which can be specialised to
classification filtering, voting filtering or partition filtering by
a proper choice of parameters.
1) Classification Filtering: The predictions of classifiers
can be used to detect mislabelled instances, what is called
classification filtering [161], [164]. For example, Thongkam
et al. [165] learn a SVM using the training data and removes
all instances which are misclassified by the SVM. A similar
method is proposed in [166] for neural networks. Miranda
et al. [167] extend the approach of [165]: four classifiers
are induced by different machine learning techniques and are
combined by voting to detect mislabelled instances. The above
methods can be applied to any classifier, but it eliminates all
instances which stand on the wrong side of the classification
boundary, what be can dangerous [168], [169]. In fact, as
discussed in [170], classification filtering (and data cleansing
in general) suffers from a chicken-and-egg dilemma, since 1)
good classifiers are necessary for classification filtering and 2)
learning in the presence of label noise may precisely produce
poor classifiers. An alternative is proposed in [169], which 1)
defines a pattern as informative if it is difficult to predict by a
model trained on previously seen data and 2) sent a pattern to
the human operator for checking if its informativeness is above
a threshold found by cross-validation. Indeed, such patterns
can either be atypical patterns that are actually informative
or garbage patterns. The level of surprise is considered to be
a good indication of how informative a pattern is, what is
quantified by the information gain log P(Y=y|X=x).
In [171], an iterative procedure called robust-C4.5 is intro-
duced. At each iteration, 1) a decision tree is inferred and
pruned by C4.5 and 2) training samples which are misclassi-
fied by the pruned decision tree are removed. The procedure
is akin to regularisation, in that the model is repeatedly made
simpler. Indeed, each iteration removes training samples, what
Fig. 2. General procedure for learning in the presence of label noise with training set cleansing, inspired by [46].
in turn allows C4.5 to produce smaller decision trees. Accuracy
is slightly improved, whereas the mean and variance of the tree
size are decreased. Hence, smaller and more stable decision
trees are obtained, which also perform better. Notice that
caution is advised when comparing sizes of decision trees
in data cleansing [172], [173]. Indeed, Oates and Jensen
[172] show that the size of decision trees naturally tends to
increase linearly with the number of instances. It means that
the removal of randomly selected training samples already
leads to a decrease in tree sizes. Therefore, Oates and Jensen
[172] propose the measure
100 ×initial tree size tree size with random filtering
initial tree size tree size with studied filtering
to estimate the percentage of decrease in tree size which is
simply due to a reduction in the number of samples. For
example, Oates and Jensen [172] show experimentally for
robust-C4.5 that 42% of the decrease in tree size can be
imputed to the sole reduction in training set size, whereas
the remaining 58% are due to an appropriate choice of the
instances to be removed. A similar analysis could be done for
other methods in this section.
Local models [174] can also be used to filter mislabelled
training samples. Such models are obtained by training a
standard model like e.g. LDA [175] or a SVM [176], [177]
on a training set consisting of the knearest neighbours of
the sample to be classified. Many local models have to be
learnt, but the respective local training sets are very small.
In [116], local SVMs are used to reject samples for which
the prediction is not confident enough. In [115], the local
SVM noise reduction method is extended for large datasets,
by reducing the number of SVMs to be trained. In [178], a
sample is removed if it is misclassified by a knearest centroid
neighbours classifier [179] trained when the sample itself is
removed from the training set.
2) Voting Filtering: Classification filtering faces the risk to
remove too many instances. In order to solve this problem,
ensembles of classifiers are used in [46], [138], [180] to
identify mislabelled instances, what is inspired by outlier
removal in regression [181]. The first step consists in using
aK-fold cross-validation scheme, which creates Kpairs of
distinct training and validation datasets. For each pair of
sets, mlearning algorithms are used to learn mclassifiers
using the training set and to classify the samples in the
validation set. Therefore, mclassifications are obtained for
each sample, since each instance belongs to exactly one
validation set. The second step consists in inferring from the
mpredictions whether a sample is mislabelled or not, what
is called voting filtering in [173] or ensemble filtering in
[164]. Two possibilities are studied in [46], [138], [180]: a
majority vote and a consensus vote. Whereas majority vote
classifies a sample as mislabelled if a majority of the m
classifiers misclassified it, the consensus vote requires that all
classifiers have misclassified the sample. One can also require
high agreement of classifiers, i.e. misclassification by more
than a given percentage of the classifiers [182]. The consensus
vote is more conservative than the majority vote and results in
fewer removed samples. The majority vote tends to throw out
too many instances [183], but performs better than consensus
vote, because keeping mislabelled instances seems to harm
more than removing too many correctly labelled samples.
The K-fold cross-validation is also used in [161]. For
each training set, a classifier is learnt and directly filters its
corresponding validation set. The approach is intermediate
between [165] and [46], [138], [180] and has been shown to
be non-selective, i.e. too many samples are detected as being
potentially noisy [161]. Eventually, Verbaeten [173] performs
an experimental comparison of some of the above methods and
proposes several variants. In particular, mclassifiers from the
same type are learnt using all combinations of the K1parts
in the training set. Voting filters are also iterated until no more
samples are removed. In [184], voting filters are obtained by
generating the mclassifiers using bagging: mtraining sets are
generated by resampling and the inferred classifiers are used
to classify all instances in the original training set.
3) Partition Filtering: Classification filtering is adapted for
large and distributed datasets in [69], [185], which proposes
a partition filter. In the first step, samples are partitioned and
rules are inferred for each partition. A subset of good rules
are chosen for each partition using two factors which measure
the classification precision and coverage for the partition. In
a second step, all samples are compared to the good rules of
all partitions. If a sample is not covered by a set of rules, it is
not classified, otherwise it is classified according to these rules.
This mechanism allows distinguishing between exceptions (not
covered by the rules) and mislabelled instances (covered by
the rules, but misclassified). Majority or consensus vote is
used to detect mislabelled instances. Privacy is preserved in
distributed datasets, since each site (or partition) only shares
its good rules. The approach is experimentally shown to be
less aggressive than [161]. In [186], partitioning is repeated
and several classifiers are learned for each partition. If all
classifiers predict the same label which is different from
the observed label, the instance is considered as potentially
mislabelled. Votes are summed over all iterations and can be
used to order the instances.
C. Model Influence and Introspection
Mislabelled instances can be detected by analysing their
impact on learning. For example, Malossini et al. [53] define
the leave-one-out perturbed classification (LOOPC) matrix
where the (i, j)entry is the label predicted for the jth training
sample if 1) the jth sample itself is removed from the training
set and 2) the label of the ith sample is flipped. The LOOPC
matrix is defined only for binary classification. Two algo-
rithms are proposed to analyse the LOOPC matrix in search
for wrong labels. The classification-stability algorithm (CL-
stability) analyses each column to detect suspicious samples:
good samples are expected to be consistently classified even
in the case of small perturbation in training data. The leave-
one-out-error-sensitivity (LOOE-sensitivity) algorithm detects
samples whose label flip improves the overall results of the
classifier. The computation of the LOOPC matrix is expensive,
but it can be afforded for small datasets. Experiments show
that CL-stability dominates LOOE-sensitivity. The approach
is extended in [187], [188].
Based on introspection, Heskes [64] proposes an online
learning algorithm for the single-unit perceptron, when labels
coming from the teacher are polluted by uniform noise. The
presented samples are accepted only when the confidence of
the learner in the presented labelled sample is large enough.
The propensity of the learner to reject suspicious labels is
called the stubbornness: the learner only accepts to be taught
when it does not contradict its own model too much. The
stubbornness of the learner has to be tuned, since discarding
too many samples may slow the learning process. An update
rule is proposed for the student self-confidence: the stubborn-
ness is increased by learner-teacher contradictions, whereas
learner-teacher agreements decrease stubbornness. The update
rule itself depends on the student carefulness, which reflects
the confidence of the learner and can be chosen to outperform
any absent-minded teacher.
D. kNearest Neighbours-Based Methods
The knearest neighbours (kNN) classifiers [189], [190]
are sensitive to label noise [94], [95], in particular for small
neighbourhood sizes [96]. Hence, it is natural that several
methods have emerged in the kNN literature for cleansing
training sets. Among these methods, many are presented as
editing methods [191], what may be a bit misleading: most
of these methods do not edit instances, but rather edit the
training set itself by removing instances. Such approaches are
also motivated by the particular computational and memory
requirements of kNN methods for prediction, which linearly
depend on the size of the training set. See e.g. [192] for
a discussion on instance selection methods for case-based
Wilson and Martinez [95], [193] provide a survey of
kNN-based methods for data cleansing, propose several new
methods and perform experimental comparisons. Wilson and
Martinez [95] show that mislabelled training instances degrade
the performances of both the kNN classifiers built on the full
training set and the instance selection methods which are not
designed to take care of label noise. This section presents
solutions from the literature and is partially based on [95],
[193]. See e.g. [194] for a comparison of several instance-
based noise reduction methods.
kNN-based instance selection methods are mainly based
on heuristics. For example, the condensed nearest neigh-
bour (CNN) rule [195] builds a subset of training instances
which allows classifying correctly all other training instances.
However, such a heuristic systematically keeps mislabelled
instances in the training set. There exist other heuristics which
are more robust to label noise. For example, the reduced
nearest neighbours (RNN) rule [196] successively removes
instances whose removal do not cause other instances to be
misclassified, i.e. it removes noisy and internal instances.
The blame-based noise reduction (BBNR) algorithm [197]
removes all instances which contribute to the misclassification
of another instance and whose removal does not cause any
instance to be misclassified. In [198], [199], instances are
ranked based on a score rewarding the patterns that contribute
to a correct classification and punishing those that provide a
wrong one. An important danger of instance selection is to
remove too many instances [200], if not all instances in some
pathological cases [95].
More complex heuristics exist in the literature; see e.g.
[113], [201] for an experimental comparison for gene ex-
pression data. For example, Wilson [202] removes instances
whose label is different from the majority label in its k= 3
nearest neighbours. This method is extended in [203] by the
all-kNN method. In [95], [193], six heuristics are introduced
and compared with other methods: DROP1-6. For example,
DROP2 is designed to reduce label noise using the notion
of instance associates, which have the instance itself in their
knearest neighbours. DROP2 removes an instance if its
removal does not change the number of its associates which are
incorrectly classified in the original training set. This algorithm
tends to retain instances which are close to the classification
boundary. In [200], generalised edition (GE) checks whether
there are at least k0samples in the locally majority class among
the kneighbours of an instance. In such a case, the instance is
relabelled with the locally majority label, otherwise it is simply
removed from the training set. This heuristic aims at keeping
only instances with strong support for their label. Barandela
and Gasca [29] show that a few repeated applications of the
GE algorithm improves results in the presence of label noise.
Other instance selection methods designed to deal with
label noise include e.g. IB3 which employs a significance
test to determine which instances are good classifiers and
which ones are believed to be noisy [204], [205]. Lorena et
al. [206] propose to use Tomek links [207] to filter noisy
instances for splice junction recognition. Different instance
selection methods are compared in [114]. In [192], a set of
instances are selected by using Fisher discriminant analysis,
while maximising the diversity of the reduced training set. The
approach is shown to be robust to label noise for a simple
artificial example. In [208], different heuristics are used to
distinguish three types of training instances: normal instances,
border samples and instances which should be misclassified
(ISM). ISM instances are such that, based on the information
in the dataset, the label assigned by the learning algorithm is
the most appropriate even though it is incorrect. For example,
one of the heuristics uses a nearest neighbours approach to
estimate the hardness of a training sample, i.e. how hard it is
to classify correctly. ISM instances are simply removed, what
results in the so-called PRISM algorithm.
E. Graph-Based Methods
Several methods in the data cleansing literature are similar
to kNN-based editing methods, except that they represent
training sets by neighbourhood graphs [209], where the in-
stances (or nodes) are linked to other close instances. The
edge between two instances can be weighted depending on
the distance between them. Such methods work directly on
the graphs to detect noisy instances. For example, S´
et al. [94] propose variants of kNN-based algorithms which
use Gabriel graphs and relative neighbourhood graphs [210],
[211]. In [212], [213], mode filters, which preserve edges and
remove impulsive noise in images, are extended to remove
label noise in datasets represented by a graph. In [209], [214],
the ith instance is characterised by its local cut edge weight
statistic Ji, which is the sum of the weights of edges linking
the instance to its neighbours with a different label. Three
types of instances are distinguished: good samples with a small
Ji,doubtful samples with an intermediate Jiand bad samples
with a large Ji. Two filtering policies are considered: 1) to
relabel doubtful samples and to remove bad samples or 2)
to relabel doubtful and bad samples using the majority class
in good neighbours (if any) and to remove doubtful and bad
samples which have no good neighbours.
F. Ensemble and Boosting-Based Methods
As discussed in Section III-A2, AdaBoost is well known
to overfit noisy datasets. Indeed, the weights of mislabelled
instances tend to become much larger than the weights of
normal instances in the late iterations of AdaBoost. Several
works presented below show that this propensity to overfitting
can be exploited in order to remove label noise.
A simple data cleansing method is proposed in [184], which
removes a given percentage of the samples with the highest
weights after miterations of AdaBoost. Experiments show
that the precision of this boosting-based algorithm is not very
good, what is attributed to the dynamics of Adaboost. In
the first iterations, mislabelled instances quickly obtain large
weights and are correctly spotted as mislabelled. However,
consequently, several correctly labelled instances then obtain
large weights in late iterations, what explains that they are
incorrectly removed from the training set by the boosting filter.
A similar approach is pursued in [215]. Outlier removal
boosting (ORBoost) is identical to AdaBoost, except that
instance weights which are above a certain threshold are set
to zero during boosting. Hence, data cleansing is performed
while learning and not after learning as in [184]. ORBoost is
sensitive to the choice of the threshold, which is performed us-
ing validation. In [216], mislabelled instances are also removed
during learning, if they are misclassified by the ensemble with
high confidence.
In [217], edge analysis is used to detect mislabelled in-
stances. The edge of an instance is defined as the sum of the
weights of weak classifiers which misclassified the instance
[218]. Hence, an instance with a large edge is often misclas-
sified by the weak learners and is classified by the ensemble
with a low confidence, what is the contrary of the margin
defined in [106]. Wheway [217] observes a homogenisation of
the edge as the number of weak classifiers increases: the mean
of the edge stabilises and its variance goes to zero. It means
that observations which were initially classified correctly are
classified incorrectly in later rounds in order to classify harder
observations correctly, what is consistent with results in [106],
[218]. Mislabelled data have edge values which remain high
due to persistent misclassification. It is therefore proposed to
remove the instances corresponding e.g. to the 5% top edge
G. Others Methods
There exist other methods for data cleansing. For example,
in ECG segmentation, Hughes et al. [56] delete the label of
the instances (and not the instances themselves) which are
close to classification boundaries, since experts are known
to be less reliable in that region. Thereafter, semi-supervised
learning is performed using both the labelled and the (newly)
unlabelled instances. In [219], a genetic algorithm approach
based on a class separability criterion is proposed. In [220],
[221], the automatic data enhancement (ADE) method and
the automatic noise reduction (ANR) method are proposed to
relabel mislabelled instances with a neural network approach.
A similar approach is proposed in [222] for decision trees.
H. Discussion
One of the advantages of label noise cleansing is that
removed instances have absolutely no effects on the model
inference step [158]. In several works, it has been observed
that simply removing mislabelled instances is more efficient
than relabelling them [167], [223]. However, instance selection
methods may remove too many instances [132]–[134], [200],
if not all instances in some pathological cases [95]. On the
one hand, Matic et al. [168] show that overcleansing may
reduce the performances of classifiers. On the other hand, it
is suggested in [46] that keeping mislabelled instances may
harm more than removing too many correctly labelled samples.
Therefore, a compromise has to be found. The overcleansing
problem is of particular importance for imbalanced datasets
[224]. Indeed, minority instances may be more likely to be
removed by e.g. classification filtering (because they are also
more likely to be misclassified), what makes learning even
more difficult. In [225], it is shown that dataset imbalance can
affect the efficiency of data cleansing methods. Label noise
cleansing can also reduce the complexity of inferred models,
but it is not always trivial to know if this reduction is not
simply due to the reduction of the training set size [172], [173].
Surprisingly, to the best of our knowledge, the method in
[56] has not been generalised to other label noise cleansing
methods, what would be easy to do. Indeed, instead of
completely removing suspicious instances, one could only
delete their labels and perform semi-supervised learning on the
resulting training set. The approach in [56] has the advantage
of keeping the distribution of the instances unaltered (what is
not the case for their conditional distributions, though), what is
of particular interest for generative approaches. An interesting
open research question is whether this method would improve
the results with respect to the classical solution of simply
removing suspicious instances. Another alternative would be
to resubmit the suspicious samples to a human expert for
relabelling as proposed in [168]. However, this may reveal
too costly or even impossible in most applications, and there
is no guarantee that the new labels will actually be noise-free.
When some information is available about label noise or
its consequences on learning, it becomes possible to design
models which take label noise into account. Typically, one can
learn a label noise model simultaneously with a classifier, what
uncouples both components of the data generation process and
improves the resulting classifier. In a nutshell, the resulting
classifier learns to classify instances according to their true,
unknown class. Other approaches consist in modifying the
learning algorithm in order to reduce the influence of label
noise. Data cleansing can also be embedded directly into the
learning algorithm, like e.g. for SVMs. Such techniques are
described in this section and are called label noise-tolerant,
since they can tolerate label noise by modelling it. Section
VII-A reviews probabilistic methods, whereas model-based
methods are discussed in Section VII-B.
A. Probabilistic Methods
Many label noise-tolerant methods are probabilistic, in a
broad sense. They include Bayesian and frequentist methods,
as well as methods based on clustering or belief functions. An
important issue which is highlighted by these methods is the
identifiability of label noise. The four families of methods are
discussed in the following four subsections.
1) Bayesian Approaches: Detecting mislabelled instances is
a challenging problem. Indeed, there are identifiability issues
[226]–[228], as illustrated in [122], where consumers answer
a survey with some error probability. Under the assumption
that it results in a Bernoulli process, it is possible to obtain an
infinite number of maximum likelihood solutions for the true
proportions of answers and the error probabilities. In other
words, in this simple example, it is impossible to identify
the correct model for observed data. Several works claim
that prior information is strictly necessary to deal with label
noise. In particular, [5], [122], [227], [228] propose to use
Bayesian priors on the mislabelling probabilities to break ties.
Label noise identifiability is also considered for inductive logic
programming in [226], where a minimal description length
principle prevents the model to overfit on label noise.
Several Bayesian methods to take care of label noise are re-
viewed in [68] and summarised here. In medical applications,
it is often necessary to assess the quality of binary diagnosis
tests with label noise. Three parameters must be estimated:
the population prevalence (i.e. the true proportion of positive
samples) and the sensitivity and specificity of the test itself [5].
Hence, the problem has one degree of freedom in excess, since
only two data-driven constraints can be obtained (linked to the
observed proportions of positive and negative samples). In [5],
[229], [230], it is proposed to fix the degree of freedom using
a Bayesian approach: setting a prior on the model parameters
disambiguates maximum likelihood solutions. Indeed, whereas
the frequentist approach considers that parameters have fixed
values, the Bayesian approach considers that all unknown
parameters have a probability distribution that reflects the
uncertainty in their values and that prior knowledge about
unknown parameters can be formally included [231]. Hence,
the Bayesian approach can be seen as a generalisation of
constraints on the parameters values, where the uncertainty
on the parameters is taken into account through priors.
Popular choices for Bayesian priors for label noise are
Beta priors [5], [128], [229], [230], [232]–[236] and Dirichlet
priors [237], [238], which are the conjugate priors of binomial
and multinomial distributions, respectively. Bayesian methods
have also been designed for logistic regression [130], [236],
[239]–[241], hidden Markov models [84] and graphical mod-
els for medical image segmentation [242]. In the Bayesian
approaches, although the posterior distribution of parameters
may be difficult (or impossible) to calculate directly, efficient
implementations are possible using Markov chain Monte Carlo
(MCMC) methods, which allow approximating the posterior
of model parameters [68]. A major advantage of using priors
is the ability to include any kind of prior information in the
learning process [68]. However, the priors should be chosen
carefully, for the results obtained depend on the quality of the
prior distribution used [243], [244].
In the spirit of the above Bayesian approaches, an iterative
procedure is proposed in [128] to correct labels. For each
sample, Rekaya et al. [235] define an indicator variable αi
which is equal to 1if the label of the ith instance was
switched. Hence, each indicator follows a Bernoulli distribu-
tion parametrised by the mislabelling rate (which itself follows
a Beta prior). In [128], the probability that αi= 1 is estimated
for each sample and the sample with the higher mislabelling
probability is relabelled. The procedure is repeated as long
as the test is significant. Indicators are also used in [245]
for Alzheimer disease prediction, where four out of sixteen
patients are detected as potentially misdiagnosed. The correc-
tion of the supposedly incorrect labels leads to a significant
increase in predictive ability. A similar approach is used in
[246] to robustify multiclass Gaussian process classification.
If the indicator for a given sample is zero, then the label of
that sample is assumed to correspond to a latent function.
Otherwise, the label is assumed to be randomly chosen. The
same priors as in [235] are used and the approach is shown to
yield better results than other methods which assume that the
latent function is polluted by a random Gaussian noise [247]
or which use Gaussian processes with heavier tails [248].
2) Frequentist Methods: Since label noise is an inherently
stochastic process, several frequentist methods have emerged
to deal with it. A simple solution consists in using mixture
models, which are popular in outlier detection [32]. In [249],
each sample is assumed to be generated either from a majority
(or normal) distribution or an anomalous distribution, with
respective priors 1λand λ. The expert error probability λis
assumed to be relativity small. Depending on prior knowledge,
any appropriate distribution can be used to model the majority
and anomalous distributions, but the anomalous distribution
may be simply chosen as uniform. The set of anomalous
samples is initially empty, i.e. all samples initially belong to
the majority set. Samples are successively tested and added
to the anomalous set whenever the increase in log-likelihood
due to this operation is higher than a predefined threshold.
Mansour and Parnas [250] also consider the mixture model
and propose an algorithm to learn conjunctions of literals.
Directly linked with the definition of NAR label noise in
Section II-C, Lawrence and Sch¨
olkopf [67] propose another
probabilistic approach to label noise. The label of an instance
is assumed to correspond to two random variables (see Fig.
3, inspired by [67]): the true hidden label Yand the observed
label ˜
Y, which is possibly noisy. ˜
Yis assumed to depend
only on the true label Y, whose relationship is described by a
labelling matrix (see Section II-C2). Using this simple model
of label noise, a Fisher discriminant is learned using an EM ap-
proach. Eventually, the approach is kernelised and is shown to
effectively deal with label noise. Interestingly, the probabilistic
modelling also leads to an estimation of the noise level. Later,
Li et al. [251] extended this model by relaxing the Gaussian
distribution assumption and carried out extensive experiments
on more complex datasets, which convincingly demonstrated
the value of explicit label noise modelling. More recently the
same model has been extended to multiclass datasets [252]
and sequential data [253]. Asymmetric label noise is also
considered in [66] for logistic regression. It is shown that
conditional probabilities are altered by label noise and that
this problem can be solved by taking a model of label noise
into account. A similar approach was developed for neural
networks in [254], [255] for uniform label noise. Repeatedly, a
neural network is trained to predict the conditional probability
of each class, what allows optimising the mislabelling prob-
ability before retraining the neural network. The mislabelling
probability is optimised either using a validation set [254] or
a Bayesian approach with a uniform prior [255]. In [256],
Gaussian processes for classification are also adapted for label
noise by assuming that each label is potentially affected by a
uniform label noise. It is shown that label noise modelling
increases the likelihood of observed labels when label noise
is actually present.
Valizadegan and Tan [257] propose a method based on a
weighted KNN. Given the probability pithat the ith training
example is mislabelled, the binary label yiis replaced by its
expected value piyi+ (1 pi)yi= (1 2pi)yi. Then, the
sum of the consistencies
δi= (1 2pi)yiPjN(xi)wij (1 2pj)yj
between the expected value of yiand the expected value of
the weighted KNN prediction is maximised, where N(xi)
contains the neighbours of xiand wij is the weight of the jth
neighbour. To avoid declaring all the examples from one of
the two classes as mislabelled, a L1regularisation is enforced
on the probabilities pi.
Contrarily to the methods described in Section VII-A1,
Bayesian priors are not used in the above frequentist methods.
Fig. 3. Statistical model of label noise, inspired by [67].
We hypothesise that the identifiability problem discussed in
Section VII-A1 is solved by using a generative approach and
setting constraints on the conditional distribution of X. For
example, in [67], Gaussian distributions are used, whereas Li
et al. [251] consider mixtures of Gaussian distributions. The
same remark applies to Section VII-A3.
3) Clustering-Based Methods: In the generative statistical
models of Section VII-A2, it is assumed that the distribution
of instances can help to solve classification problems. Classes
are not arbitrary: they are linked to a latent structure in the
distribution of X. In other words, clusters in instances can
be used to build classifiers, what is done in [136]. Firstly,
a clustering of the instances [258] is performed using an
unsupervised algorithm. Labels are not used and the procedure
results in a mixture of Kmodels pk(x)with priors πkfor
components k= 1 . . . K. Secondly, instances are assumed to
follow the density
p(x) = X
ryk πkpk(x)(10)
where ryk can be interpreted as the probability that the kth
cluster belongs to the yth class. The coefficients ryk are
learned using a maximum likelihood approach. Eventually,
classification is performed by computing the conditional prob-
abilities P(Y=y|X=x)using both the unsupervised
(clusters) and supervised (ryk probabilities) parts of the model.
When a Gaussian mixture model is used to perform clustering,
the mixture model can be interpreted as a generalisation
of mixture discriminant analysis (MDA, see [259]). In this
case, the model is called robust mixture discriminant analysis
(RMDA) and is shown to improve classification results with
respect to MDA [136], [260]. In [261], the method is adapted
to discrete data for DNA barcoding and is called robust dis-
crete discriminant analysis. In that case, data are modelled by
a multivariate multinomial distribution. A clustering approach
is also used in [262] to estimate a confidence on each label,
where each instance inherits the distribution of classes within
its assigned cluster. Confidences are averaged over several
clusterings and a weighted training set is obtained.
In this spirit, El Gayar et al. [263] propose a method which
is similar to [136]. Labels are converted into soft labels in
order to reflect the uncertainty on labels. Firstly, a fuzzy
clustering of the training instances is performed, which gives
a set of cluster and the membership of each instance to each
cluster. Then, the membership Lyk of the kth cluster to the
yth class is estimated using the fuzzy memberships. Each
instance with label yincreases the membership Lyk by its
own membership to cluster k. Eventually, the fuzzy label of
each instance is computed using the class memberships of
the clusters where the instance belongs. Experiments show
improvements with respect to other label fuzzification methods
like kNN soft labels and Keller soft labels [264].
4) Belief Functions: In the belief function theory, each
possible subset of classes is characterised by a belief mass,
which is the amount of evidence which supports the subset
of classes [265]. For example, let us consider an expert who
1) thinks that a given case is positive, but 2) has a very
low confidence in its own prediction. In the formalism of
belief functions, one can translate the above judgement by
a belief mass function (BMF, also called basic probability
assignment) msuch that m({−1,+1})=0.8,m({−1})=0
and m({+1})=0.2. Here, there is no objective uncertainty
on the class itself, but rather a subjective uncertainty on
the judgement itself. For example, if a coin is flipped, the
BMF would simply be m({head,tail}) = 1,m({head}) = 0
and m({tail})=0when the bias of the coin is unknown.
If the coin is known to be unbiased, the BMF becomes
m({head,tail})=0,m({head}) = 1
2and m({tail}) = 1
Again, this simple example illustrates how the belief func-
tion theory allows distinguishing subjective uncertainty from
objective uncertainty. Notice that Smets [266] argues that it
is necessary to fall back to classical probabilities in order
to make decisions. Different decision rules are analysed in
[79]. Interestingly, the belief function formalism can be used
to modify standard machine learning methods like e.g. kNN
classifiers [78], neural networks [80], decision trees [267],
mixture models [268], [269] or boosting [270].
In the context of this survey, belief functions cannot be
used directly, since the belief masses are not available. Indeed,
they are typically provided by the expert itself as an attempt
to quantify its own (lack of) confidence, but we made the
hypothesis in Section I that such information is not available.
However, several works have proposed heuristics to infer belief
masses directly from data [78], [80], [271].
In [78], a kNN approach based on Dempster-Shafer theory
is proposed. If a new sample xshas to be classified, each
training sample (xi, yi)is considered as an evidence that the
class of xsis yi. The evidence is represented by a BMF ms,i
such that ms,i({yi}) = α,ms,i (Y)=1αand ms,i is zero
for all other subsets of classes, where
such that 0< α0<1and Φis a monotonically decreasing
function of the distance ds,i between both instances. There are
many possible choices for Φ;
Φ(d) = exp γdβ(12)
is chosen in [78], where γ > 0and β∈ {1,2}. Heuristics
are proposed to select proper values of α0and γ. For the
classification of the new sample xs, each training sample
provides an evidence. These evidences are combined using the
Dempster rule and it becomes possible to take a decision (or
to refuse to take a decision if the uncertainty is too high). The
case of mislabelling is experimentally studied in [78], [272]
and the approach is extended to neural networks in [80].
In [271], a kNN approach is also used to infer BMFs.
For a given training sample, the frequency of each class in
its knearest neighbours is computed. Then, the sample is
assigned to a subset of classes containing 1) the class with
the maximum frequency and 2) the classes whose frequency
is not too different from the maximum frequency. A neural
network is used to compute beliefs for test samples.
B. Model-Based Methods
Apart from probabilistic methods, specific strategies have
been developed to obtain label noise-tolerant variants of
popular learning algorithms, including e.g. support vector
machines, neural networks and decision trees. Many publi-
cations also propose label noise-tolerant boosting algorithms,
since boosting techniques like AdaBoost are well-known to be
sensitive to label noise. Eventually, label noise is also tackled
in semi-supervised learning. These five families of methods
are discussed in the following five subsections.
1) Support Vector Machines and Robust Losses: SVMs are
not robust to label noise [62], [82], even if instances are
allowed to be misclassified during learning. Indeed, instances
which are misclassified during learning are penalised in the
objective using the hinge loss
[1 yihxi, wi]+(13)
where [z]+= max(0, z)and wis the weight vector. The hinge
loss increases linearly with the distance to the classification
boundary and is therefore significantly affected by mislabelled
instances which stand far from the boundary.
Data cleansing can be directly implemented into the learning
algorithm of SVMs. For example, instances which correspond
to very large dual weights can be identified as potentially
mislabelled [273]. In [274], ksamples are allowed to be not
taken into account in the objective function. For each sample,
a binary variable (indicating whether or not to consider the
sample) is added and the sum of the indicators is constrained
to be equal to k. An opposite approach is proposed in [275]
for aggregated training sets, which consists of several distinct
training subsets labelled by different experts. The percentage
of support vectors in training samples is constrained to be
identical in each subset, in order to decrease the influence
of low-quality teachers which tend to require more support
vectors due to more frequent mislabelling. In [276], [277],
SVMs are adapted by weighting the contribution of each
training sample in the objective function. The weights (or
fuzzy memberships) are computed using heuristics. Similar
work is done in [278] for relevance vector machines (RVMs).
Empathetic constraints SVMs [279] relax the constraints of
suspicious samples in the SVM optimisation problem.
Xu et al. [280] propose a different approach, which consists
in using the loss
ηi[1 yihxi, wi]++ (1 ηi)(14)
where 0ηi1indicates whether the ith sample is an
outlier. The ηivariables must be optimised together with the
weights vector, what is shown to be equivalent to using the
robust hinge loss
min(1,[1 yihxi, wi]+).(15)
Notice that there exist other bounded, non-convex losses
[281]–[284] which could be used similarly. A non-convex loss
is also used in [285] to produce label noise-tolerant SVMs
without filtering. For binary classification with y∈ {−1,+1},
the loss is
Kpe(1 pe(yi)) [1 yihxi, wi]+pe(yi) [1 + yihxi, wi]+
where Kpe=1
1pe(+1)pe(1) . Interestingly, the expected
value of the proposed loss (with respect to all possible mis-
labellings of the noise-free training set) is equal to the hinge
loss computed on the noise-free training set. In other words,
it is possible to estimate the noise-free [. . .] errors from the
noisy data. Theoretical guarantees are given and the proposed
approach is shown to outperform SVMs, but error probabilities
must be known a priori.
2) Neural Networks: Different label noise-tolerant variants
of the perceptron algorithm are reviewed and compared exper-
imentally in [286]. In the standard version of this algorithm,
samples are presented repeatedly (on-line) to the classifier. If
a sample is misclassified, i.e.
where wis the weight vector and bis the bias, then the
weight vector is adjusted towards this sample. Eventually, the
perceptron algorithm converges to a solution.
Since the solution of the perceptron algorithm can be biased
by mislabelled samples, different variants have been designed
to reduce the impact of mislabelling. With the λ-trick [287],
[288], if an instance has already been misclassified, the adapta-
tion criterion becomes yi[wxi+b]+λkxik2
2<0. Large values
of λmay prevent mislabelled instances to trigger updates.
Another heuristic is the α-bound [289], which does not update
wfor samples which have already been misclassified αtimes.
This simple solution limits the impact of mislabelled instances.
Although not directly designed to deal with mislabelling,
Khardon and Wachman [286] also describe the perceptron
algorithm using margins (PAM, see [290]). PAM updates w
for instances with yi[wxi+b]< τ , similarly to support vector
classifiers and to the λ-trick.
3) Decision Trees: Decision trees can easily overfit data, if
they are not pruned. In fact, learning decision trees involves
a trade-off between accuracy and simplicity, which are two
requirements for good decision trees in real-world situations
[291]. It is particularly important to balance this trade-off
in the presence of label noise, what makes the overfitting
problem worse. For example, Clark and Niblett [291] propose
the CN2 algorithm which learns a disjunction of logic rules
while avoiding too complex ones.
4) Boosting Methods: In boosting, an ensemble of weak
learners htwith weights αtis formed iteratively using a
weighted training set. At each step t, the weights w(t)
iof mis-
classified instances are increased (resp. decreased for correctly
classified samples), what progressively reduces the ensemble
training error because the next weak learners focus on the
errors of the previous ones. As discussed in Section III,
boosting methods tend to overfit label noise. In particular,
AdaBoost obtains large weights for mislabelled instances in
late stages of learning. Hence, several methods propose to
update weights more carefully to reduce the sensitivity of
boosting to label noise. In [292], MadaBoost imposes an upper
bound for each instance weight, which is simply equal to the
initial value of that weight. The AveBoost and AveBoost2
[293], [294] algorithms replace the weight w(t+1)
iof the ith
instance at step t+ 1 by
t+ 1 .(18)
With respect to AdaBoost, AveBoost2 obtains larger train-
ing errors, but smaller generalisation errors. In other words,
AveBoost2 is less prone to overfitting than AdaBoost, what
improves results in the presence of label noise. Kim [295]
proposes another ensemble method called Averaged Boosting
(A-Boost), which 1) does not take instances weights into ac-
count to compute the weights of the successive weak classifiers
and 2) performs similarly to bagging on noisy data. Other
weighting procedures have been proposed in e.g. [296], but
they were not assessed in the presence of label noise.
In [297], two approaches are proposed to reduce the con-
sequences of label noise in boosting. Firstly, AdaBoost can
be early-stopped: limiting the number of iterations prevents
AdaBoost from overfitting. A second approach consists in
smoothing the results of AdaBoost. The proposed BB algo-
rithm combines bagging and boosting: 1) Ktraining sets
consisting of ρpercents of the training set (sub-sampled with
replacement) are created, 2) Kboosted classifiers are trained
for Miterations and 3) the predictions are aggregated. In
[297], it is advised to use K= 15,M= 15 and ρ=1
The BB algorithm is shown to be less sensitive to label noise
than AdaBoost. A similar approach is proposed in [298]: the
multiple boosting (MB) algorithm.
Areverse boosting algorithm is proposed in [299]. In adap-
tive boosting, weak learners may have difficulties to obtain
good separation frontiers because correctly classified samples
get lower and lower weights as learning goes on. Hence,
safe, noisy and borderline patterns are distinguished, whose
weights are respectively increased, decreased and unaltered
during boosting. Samples are classified into these three cate-
gories using parallel perceptrons, a specific type of committee
machine whose margin allows to separate the input space into
three regions: a safe region (beyond the margin), a noisy
region (before the margin) and a borderline region (inside
the margin). The approach improves the results of parallel
perceptrons in the presence of label noise, but is most often
dominated by classical perceptrons.
5) Semi-Supervised Learning: In [7], a particle
competition-based algorithm is proposed to perform semi-
supervised learning in the presence of label noise. Firstly,
the dataset is converted into a graph, where instances are
nodes with edges between similar instances. Each labelled
node is associated with a labelled particle. Particles walk
through the graph and cooperate with identically-labelled
particles to label unlabelled instances, while staying in the
neighbourhood of their home node. What interests us in [7]
is the behaviour of mislabelled particles: they are pushed
away by the particles of near instances with different labels,
what prevents a mislabelled instance to influence the label of
close unlabelled instances. In [300], unlabelled instances are
firstly labelled using a semi-supervised learning algorithm,
then the new labels are used to filter instances. Similarly,
context-sensitive semi-supervised support vector machines
[301], [302] first use labelled instances to label unlabelled
instances which are spatially close (e.g. in images) to them
and second these new semilabels are used to reduce the
effect of mislabelled training instances. Other works on
label noise for semi-supervised learning include e.g. [303]
or [304]–[306], which are particular because they model the
label noise induced by the labelling of unlabelled samples. A
similar problem occur in co-training [307]–[309] where two
different views are available for each instance, like e.g. the
text in a web page and the text attached to the hyperlinks
pointing to this page. In the seminal work of Blum and
Mitchell [307], co-training consists in 1) learning two distinct
weak predictors from labelled data with each of the two
views, 2) predicting labels with the weak predictors for a
random subset of the unlabelled data and 3) keeping the most
confident labels to enlarge the pool of labelled instances. See
e.g. [310]–[314] for examples of studies on the effectiveness
of co-training. Co-training allows each weak predictor to
provide labels to improve the other weak predictor, but
the problem is that each weak predictor is likely to make
prediction errors. Incorrect labels are a source of label noise
which has to be taken into account, like e.g. in [308], [309].
C. Discussion
The probabilistic methods to deal with label noise are
grounded in a more theoretical approach than robust or data
cleansing methods. Hence, probabilistic models of label noise
can be directly used and allow to take advantage of prior
knowledge. Moreover, the model-based label noise-tolerant
methods allow us to use the knowledge gained by the analysis
of the consequences of label noise. However, the main problem
of the approaches described in this section is that they increase
the complexity of learning algorithms and can lead to over-
fitting, because of the additional parameters of the training
data model. Moreover, the identifiability issue discussed in
Section VII-A1 must be addressed, what is done explicitly in
the Bayesian approach (using Bayesian priors) and implicitly
in the frequentist approach (using generative models).
As highlighted in [1], different models should be used for
training and testing in the presence of label noise. Indeed,
a complete model of the training data consists of a label
noise model and a classification model. Both parts are used
during training, but only the classification model is useful for
prediction: one has no interest in making noisy predictions.
Dropping the label noise model is only possible when label
noise is explicitly modelled, as in the probabilistic approaches
discussed in Section VII-A. For other approaches, the learning
process of the classification model is supposed to be robust or
tolerant to label noise and to produce a good classification
This section discusses how experiments are performed in
the label noise literature. In particular, existing datasets, label
noise generation techniques and quality measures are high-
A. Datasets with Identified Mislabelled Instances and Label
Noise Generation Techniques
There exist only a few datasets where incorrect labels have
been identified. Among them, Lewis et al. [315] provide
a version of the Reuters dataset with corrected labels and
Malossini et al. [53] propose a short analysis of the reliability
of instances for two microarray datasets. In spam filtering,
where the expert error rate is usually between 3% and 7%,
the TREC datasets have been carefully labelled by experts
adhering to the same definition of spam, with a resulting
expert error rate of about 0.5% [127]. Mislabelling is also
discussed for a medical image processing application in [316]
and Alzheimer disease prediction in [245]. However, artificial
label noise is more common in the literature. Most studies on
label noise use NCAR label noise, which is introduced in real
datasets by 1) randomly selecting instances and 2) changing
their label into one of the other remaining labels [135]. In
this case, label noise is independent of Y. In [317], it is also
proposed to simulate label noise for artificial datasets by 1)
computing the membership probabilities P(Y=y|X=x)
for each training sample x, 2) adding a small uniform noise
to these values and 3) choosing the label corresponding to the
largest polluted membership probability.
Several methods have been proposed to introduce NAR
label noise. For example, in [62], label noise is artificially
introduced by changing the labels of some randomly chosen
instances from the majority class. In [3], [69], [301], label
noise is introduced using a pairwise scheme. Two classes c1
and c2are selected, then each instance of class c1has a
probability Peto be incorrectly labelled as c2and vice versa.
In other words, this label noise models situations where only
certain types of classes are mislabelled. In [1], label noise
is introduced by increasing the entropy of the conditional
mass function P(˜
Y|X). The proposed procedure is called
majorisation: it leaves the probability of the majority class
unchanged, but the remaining probability is spread more
evenly on the other classes, with respect to the true conditional
mass function P(Y|X). In [151], [153], the percentage of
mislabelled instances is firstly chosen, then the proportions
of mislabelled instances in each class are fixed.
NNAR label noise is considered in much less works than
NCAR and NAR label noise. For example, Chhikara and
McKeon [72] introduce the truncated and the exponential label
noise models which are detailed in Section III-A3 and where
the probability of mislabelling depends on the distance to the
classification boundary. A special case of truncated label noise
is studied in [70]. In [81], two features are randomly picked
and the probability of mislabelling depends on which quadrant
(with respect to the two selected features) the sample belongs
In practice, it would be very interesting to obtain more
real-world datasets where mislabelled instances are clearly
identified. Also, an important open research problem is to find
what the characteristics of real-world label noise are. Indeed,
it is not yet clear in the literature if and when NCAR, NAR
or NNAR label noise is the most realistic.
B. Validation and Test of Algorithms in the Presence of Label
An important issue for methods which deal with label noise
is to prove their efficiency. Depending on the consequence of
label noise which is targeted, different criteria can be used. In
general, a good method must either 1) maintain the value of the
quality criterion when label noise is introduced or 2) improve
the value of the criterion with respect to other methods in the
presence of label noise. In the literature, most experiments
assess the efficiency of methods to take care of label noise
in terms of accuracy (see e.g. [46], [69], [138], [160], [161],
[171], [180], [184]), since a decrease in accuracy is one of
the main consequences of label noise, as discussed in Section
Another common criterion is the model complexity [46],
[138], [184], e.g. the number of nodes for decision trees or
the number of rules in inductive logic. Indeed, as discussed in
Section III-B, some inference algorithms tend to overfit in the
presence of label noise, what results in overly complex models.
Less complex models are considered better, since they are less
prone to overfitting.
In some contexts, the estimated parameters of the models
themselves can also be important, as discussed in Section
III-C. Several works focus on the estimation of true frequen-
cies from observed frequencies [4], [122], [123], [126], what
is important e.g. in disease prevalence estimation.
Eventually, in the case of data cleansing methods, one can
also investigate the filter precision. In other words, do the
removed instances actually correspond to mislabelled instances
and conversely ? Different measures are used in the literature,
which can be explained using Fig. 4 inspired by [46], [138].
In [46], [69], [180], [184], [318], two types of errors are
distinguished. Type 1 errors are correctly labelled instances
which are erroneously removed. The corresponding measure
ER1=# of correctly labelled instances which are removed
# of correctly labelled instances .
Type 2 errors are mislabelled instances which are not removed.
The corresponding measure is
ER2=# of mislabelled instances which are not removed
# of mislabelled instances .
The percentage of removed samples which are actually mis-
labelled is also computed in [46], [69], [180], [183], [184],
[318], what is given by the noise elimination precision
NEP =# of mislabelled instances which are removed
# of removed instances .
Fig. 4. Types of errors in data cleansing for label noise, inspired by [46],
A good data cleansing method must find a compromise be-
tween ER1, ER2and NEP [46], [69], [180], [184]. On the
one hand, conservative filters remove few instances and are
therefore precise (ER1is small and NEP is large), but they
tend to keep most mislabelled instances (ER2is large). Hence,
classifiers learnt with data cleansed by such filters achieve
low accuracies. On the other hand, aggressive filters remove
more mislabelled instances (ER2is small) in order to increase
the classification accuracy, but they also tend to remove too
many instances (ER1is large and NEP is small). Notice that
Verbaeten and Van Assche [184] also compute the percentage
of mislabelled instances in the cleansed training set.
Notice that a problem which is seldom mentioned in the
literature is that model validation can be difficult in the
presence of label noise. Indeed, since validation data are also
polluted by label noise, methods like e.g. cross-validation or
bootstrap may poorly estimate generalisation errors and choose
meta-parameters which are not optimal (with respect to clean
data). For example, the choice of the regularisation constant in
regularised logistic regression will probably be affected by the
presence of mislabelled instances far from the classification
boundary. We think that this is an important open research
This survey shows that label noise is a complex phe-
nomenon with many potential consequences. Moreover, there
exist many different techniques to address label noise, which
can be classified as label noise-robust methods, label noise
cleansing methods or label noise-tolerant methods. As dis-
cussed in Section VII-A1, an identification problem occurs
in practical inference: mislabelled instances are difficult to
distinguish from correctly labelled instances. In fact, without
additional information beyond the main data, it is not possible
to take into account the effect of mislabelling [84]. A solution
is to make assumptions which allow selecting a compromise
between naively using instances as they are and seeing any
instance as possibly mislabelled.
All methods described in this survey can be interpreted
as making particular assumptions. Firstly, in label noise-
robust methods described in Section V, overfitting avoidance
is assumed to be sufficient to deal with label noise. In other
words, mislabelled instances are assumed to cause overfitting
in the same way as any other instance would. Secondly, in data
cleansing methods presented in Section VI, different heuristics
are used to distinguish mislabelled instances from exceptions.
Each heuristic is in fact a definition of what is label noise.
Thirdly, label noise-tolerant methods described in Section
VII impose different constraint using e.g. Bayesian priors or
structural constraints (i.e. in generative methods) or attempt to
make existing methods less sensitive to the consequences of
label noise.
In conclusion, the machine learning practitioner has to
choose the method whose definition of label noise seems more
relevant in his particular field of application. For example,
if experts can provide prior knowledge about the values of
the parameters or the shape of the conditional distributions,
probabilistic methods should be used. On the other hand, if
label noise is only marginal, label noise-robust methods could
be sufficient. Eventually, most data cleansing methods are easy
to implement and have been shown to be efficient and to
be good candidates in many situations. Moreover, underlying
heuristics are usually intuitive and easy-to-interpret, even for
the non-specialist who can look at removed instances.
There are many open research questions related to label
noise and many avenues remain to be explored. For example,
to the best of our knowledge, the method in [56] has not been
generalised to other label noise cleansing methods. Hughes
et al. delete the label of the instances (and not the instances
themselves) whose labels are less reliable and perform semi-
supervised learning using both the labelled and the (newly)
unlabelled instances. This approach has the advantage of
not altering the distribution of the instances and it could
be interesting to investigate whether this improve the results
with respect to simply removing suspicious instances. Also, it
would be very interesting to obtain more real-world datasets
where mislabelled instances are clearly identified, since there
exist only a few such datasets [53], [127], [245], [315], [316].
It is also important to find what the characteristics of real-
world label noise are, since it is not yet clear if and when
NCAR, NAR or NNAR label noise is the most realistic.
Answering this question could lead to more complex and
realistic models of label noise in the line of e.g. [5], [56], [67],
[70]–[72], [90], [91], [122], [227]–[230], [235], [251]. Label
noise should be also be studied in more complex settings than
standard classification, like e.g. image processing [242], [301],
[302] and sequential data analysis [84], [253]. The problem
of meta-parameter selection in the presence of label noise is
also an important open research problem, since estimated error
rates are also biased by label noise [112], [126], [127].
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ıt Fr´
enay received the Engineer’s degree from
the Universit´
e catholique de Louvain (UCL), Bel-
gium, in 2007. He is now Ph.D. student at the
UCL Machine Learning Group. His main research
interests in machine learning include support vector
machines, extreme learning, graphical models, clas-
sification, data clustering, probability density esti-
mation, feature selection and label noise.
Michel Verleysen received the M.S. and Ph.D.
degrees in electrical engineering from the Universit´
catholique de Louvain (Belgium) in 1987 and 1992,
respectively. He is Full Professor at the Univer-
e catholique de Louvain, and Honorary Research
Director of the Belgian F.N.R.S. (National Fund
for Scientific Research). He is editor-in-chief of
the Neural Processing Letters journal (published by
Springer), chairman of the annual ESANN con-
ference (European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine
Learning), past associate editor of the IEEE Trans. on Neural Networks
journal, and member of the editorial board and program committee of several
journals and conferences on neural networks and learning. He is author or co-
author of more than 250 scientific papers in international journals and books or
communications to conferences with reviewing committee. He is the co-author
of the scientific popularization book on artificial neural networks in the series
”Que Sais-Je?”, in French, and of the ”Nonlinear Dimensionality Reduction”
book published by Springer in 2007. His research interests include machine
learning, artificial neural networks, self-organization, time-series forecasting,
nonlinear statistics, adaptive signal processing, and high-dimensional data
... Mislabelling occurs when the categorical variable has incorrect labels for some of the data points. It is important to be aware of this risk, as the inclusion of mislabelled data decreases accuracy [50,51]. A potential example of mislabelling in embryology is evident in two embryo selection models with different labels for classification. ...
This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.
... Our approach incorporates a similar line of work by providing noisy text to a pretrained model which incorporates prior knowledge from general-domain text and small in-domain text (Peng et al., 2020;Chen et al., 2019;Harkous et al., 2020) and use it as a weak annotator similar to Ratner et al. (2020). The primary challenge with the synthetic data is the noise introduced during the generation process, and the noisy labels tend to bring little to no improvement (Frénay and Verleysen, 2013). To train on such noisy data, we employ three step training process: a) pre-training b) generate data with weighted categories c) fine-tuning similar to Chang et al. (2021a); Dehghani et al. (2017). ...
... This includes, for example, the imputation ("guessing") of missing values and noise identification. Since the quality of the results of ADM systems strongly depends on the data quality, any impairment of this, for example, by noise, 4 must be taken into account (Frénay and Verleysen, 2013). Thus, many approaches have been developed to tackle the problem (Luengo et al., 2012) which all have their pros and cons, like artificially creating values based on statistical evaluation of other values, or completely removing instances with missing values (Little and Rubin, 2019). ...
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Several governmental organizations all over the world aim for algorithmic accountability of artificial intelligence systems. However, there are few specific proposals on how exactly to achieve it. This article provides an extensive overview of possible transparency and inspectability mechanisms that contribute to accountability for the technical components of an algorithmic decision-making system. Following the different phases of a generic software development process, we identify and discuss several such mechanisms. For each of them, we give an estimate of the cost with respect to time and money that might be associated with that measure.
... Research focusing on learning with noisy labels (LNL) has been widely carried out (Frenay & Verleysen, 2014;Song et al., 2022), such as remote sensing, medical image analysis, and textual classification (Mellor et al., 2015;Karimi et al., 2020;Qiao et al., 2022). There are two current mainstream trends: one is the filtering of noise labeled data, and the other is the design and construction of noise robust algorithms (Yuan et al., 2018). ...
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State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.
Since dirty data have negative influence on the accuracy of machine learning models, the relation between data quality and model results could be used in the selection of the proper model and data cleaning strategies. However, rare work has focused on this topic. Motivated by this, this chapter compares the impacts of missing, inconsistent, and conflicting data on basic classification and clustering models. Based on the evaluation observations, we suggest users how to select appropriate classification and clustering models and clean dirty data in the database and machine learning communities. Section 2.1 gives the research motivation of this chapter. Section 2.2 describes our assessment methodology. Section 2.3 presents our experimental results and analyses. We discuss the lessons learned from the evaluation in Sect. 2.4 and provide strategies of model selection and data cleansing, followed by a brief summary in Sect. 2.5.
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There is an increasing gap between fast growth of data and the limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve high-value content from data more effectively. In this work, we propose an interactive data exploration system as a new database service, using an approach called “explore-by-example.” Our new system is designed to assist the user in performing highly effective data exploration while reducing the human effort in the process. We cast the explore-by-example problem in a principled “active learning” framework. However, traditional active learning suffers from two fundamental limitations: slow convergence and lack of robustness under label noise. To overcome the slow convergence and label noise problems, we bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. Evaluation results using real-world datasets and user interest patterns show that our new system, both in the noise-free case and in the label noise case, significantly outperforms state-of-the-art active learning techniques and data exploration systems in accuracy while achieving the desired efficiency for interactive data exploration.
As a new programming paradigm, deep learning has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and deep learning are normally black box systems, and even worse deep learning based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. While various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations and until today this problem is still of considerable interest. In this work, we first propose a novel characterization to distinguish abnormal examples from normal ones based on the observation that abnormal examples have significantly different (adversarial) robustness from normal ones. We systemically analyze those three different types of abnormal samples in terms of robustness, and find that they have different characteristics from normal ones. As robustness measurement is computationally expensive and hence can be challenging to scale to large networks, we then propose to effectively and efficiency measure robustness of an input sample using the cost of adversarially attacking the input, which was originally proposed to test robustness of neural networks against adversarial examples. Next, we propose a novel detection method, named “attack as detection” (A ² D) which uses the cost of adversarially attacking an input instead of robustness to check if it is abnormal. Our detection method is generic and various adversarial attack methods could be leveraged. Extensive experiments show that A ² D is more effective than recent promising approaches that were proposed to detect only one specific type of abnormal examples. We also thoroughly discuss possible adaptive attack methods to our adversarial example detection method and show that A ² D is still effective in defending carefully designed adaptive adversarial attack methods, e.g., the attack success rate drops to 0% on CIFAR10.
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Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.
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To develop a robust classification algorithm in the adversarial setting, it is important to understand the adversary's strategy. We address the problem of label flips attack where an adversary contaminates the training set through flipping labels. By analyzing the objective of the adversary, we formulate an optimization framework for finding the label flips that maximize the classification error. An algorithm for attacking support vector machines is derived. Experiments demonstrate that the accuracy of classifiers is significantly degraded under the attack.
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
Fisher‐Rao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in non‐normal settings, especially when the classes are clustered. Low dimensional views are an important by‐product of LDA—our new techniques inherit this feature. We can control the within‐class spread of the subclass centres relative to the between‐class spread. Our technique for fitting these models permits a natural blend with nonparametric versions of LDA.