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Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing multiple passes over data. Several works have been devoted to this area, either directly or indirectly as characteristics of big data processing, i.e., Velocity and Volume. Given the current industry needs, there are many challenges to be addressed before existing methods can be efficiently applied to real-world problems. In this work, we focus on elucidating the connections among the current stateof- the-art on related fields; and clarifying open challenges in both academia and industry. We treat with special care topics that were not thoroughly investigated in past position and survey papers. This work aims to evoke discussion and elucidate the current research opportunities, highlighting the relationship of different subareas and suggesting courses of action when possible.
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Machine learning for streaming data: state of the art,
challenges, and opportunities
Heitor Murilo Gomes
University of Waikato
Hamilton, New Zealand
LTCI, T´
el´
ecom ParisTech,
IP-Paris
Paris, France
heitor.gomes@waikato.ac.nz
Jesse Read
LIX, ´
Ecole Polytechnique
Palaiseau, France
jesse.read@
polytechnique.edu
Albert Bifet
University of Waikato
Hamilton, New Zealand
LTCI, T´
el´
ecom ParisTech,
IP-Paris
Paris, France
albert.bifet@waikato.ac.nz
Jean-Paul Barddal
PPGIA, Pontifical Catholic
University of Parana
Curitiba, Brazil
jean.barddal@
ppgia.pucpr.br
Jo˜
ao Gama
LIAAD, INESC TEC
University of Porto
Porto, Portugal
jgama@fep.up.pt
ABSTRACT
Incremental learning, online learning, and data stream learn-
ing are terms commonly associated with learning algorithms
that update their models given a continuous influx of data
without performing multiple passes over data. Several works
have been devoted to this area, either directly or indirectly
as characteristics of big data processing, i.e., Velocity and
Volume. Given the current industry needs, there are many
challenges to be addressed before existing methods can be ef-
ficiently applied to real-world problems. In this work, we fo-
cus on elucidating the connections among the current state-
of-the-art on related fields; and clarifying open challenges
in both academia and industry. We treat with special care
topics that were not thoroughly investigated in past posi-
tion and survey papers. This work aims to evoke discus-
sion and elucidate the current research opportunities, high-
lighting the relationship of different subareas and suggesting
courses of action when possible.
1. INTRODUCTION
Data sources are becoming increasingly ubiquitous and faster
in comparison to the previous decade. These characteristics
motivated the development of several machine learning al-
gorithms for data streams. We are now on the verge of
moving out these methods from the research labs to the in-
dustry, similarly to what happened to traditional machine
learning methods in the recent past. This current movement
requires the development and adaptation of techniques that
are adjacent to the learning algorithms, i.e., it is necessary
to develop not only efficient and adaptive learners, but also
methods to deal with data preprocessing and other prac-
tical tasks. On top of that, there is a need to objectively
reassess the underlying assumptions of some techniques de-
veloped under hypothetical scenarios to clearly understand
when and how they are applicable in practice.
Machine learning for streaming data research yielded sev-
eral works on supervised learning [19], especially classifica-
tion, mostly focused on addressing the problem of changes
to the underlying data distribution over time, i.e., concept
drifts [119]. In general, these works focus on one specific
challenge: develop methods that maintain an accurate de-
cision model with the ability to learn and forget concepts
incrementally.
The focus given to supervised learning has shifted towards
other tasks in the past few years, mostly to accommodate
more general requirements of real-world problems. Nowa-
days one can find work on data stream clustering, pattern
mining, anomaly detection, feature selection, multi-output
learning, semi-supervised learning, novel class detection, and
others. Nevertheless, some fields were less developed than
others, e.g., drift detection and recovery has been thoroughly
investigated for streaming data where labels are immedi-
ately (and fully) available. In contrast, not as much re-
search has been conducted on the efficiency of drift detec-
tion methods for streaming data where labels arrive with a
non-negligible delay or when some (or all) labels never arrive
(semi-supervised and unsupervised learning).
Previous works have shown the importance of some of these
problems and their research directions. Krempl et al. [73]
discuss important issues, such as how to evaluate data stream
algorithms, privacy and the gap between algorithms to full
decision support systems. Machine learning for data streams
is a recurrent topic in Big Data surveys [44; 127] as it is re-
lated to the Velocity and Volume characteristics of the tra-
ditional 3V’s of big data (Volume, Variety, Velocity). Nev-
ertheless, there is no consensus about how learning from
streaming data should be tackled, and depending on the
application (and the research group) different abstractions
and solutions are going to be used. For example, Stoica
et al. [115] discuss continual learning, and from their point
of view, learning from heterogeneous environments where
changes are expected to occur is better addressed by rein-
forcement learning. In summary, some reviews and surveys
focus on specific tasks or techniques, such as rule mining
for data streams [66], activity recognition [1] or ensemble
learning [53].
In this paper, we focus on providing an updated view of
the field of machine learning for data streams, highlighting
the state-of-the-art and possible research (and development)
opportunities. Our contributions focus on aspects that were
not thoroughly discussed before in similar works [73], and
thus, when appropriate, we direct the readers to works that
better introduce the original problems while we highlight
more complex challenges.
In summary, the topics discussed are organized as follows.
The first three sections of the manuscript address the im-
portant topics of preprocessing (section 2), learning (section
3), and adaptation (section 4). In the last three sections,
we turn our attention to evaluating algorithms (section 5),
streaming data and related topics in AI (section 6), and
existing tools for exploring machine learning for streaming
data (section 7). Finally, section 8 outlines the main take-
aways regarding research opportunities and challenges dis-
cussed throughout this paper.
2. DATA PREPROCESSING
Data preparation is an essential part of a machine learning
solution. Real-world problems require transformations to
raw data, preprocessing steps and usually a further selection
of ‘relevant’ data before it can be used to build machine
learning models.
Trivial preprocessing, such as normalizing a feature, can be
complicated in a streaming setting. The main reason is that
statistics about the data are unknown a priori, e.g., the
minimum and maximum values a given feature can exhibit.
There are different approaches to scale and discretize fea-
tures as discussed in the next subsections; still, as we move
into more complex preprocessing, it is usually unknown ter-
ritory or one which has not been explored in depth.
There are mainly two reasons for performing data prepro-
cessing before training a machine learning model:
1. To allow learning algorithms to handle the data;
2. To improve learning by extracting or keeping only the
most relevant data.
The first reason is more restrictive, as some algorithms will
not be able to digest data if it is not in the expected format,
i.e., the data types do not match. Other algorithms will
perform poorly if the data is not normalized. Examples
include algorithms that rely on Stochastic Gradient Descent
and distance-based algorithms (such as nearest neighbors).
Feature engineering governs the second aspect, as accurate
machine learning solutions often rely on a well-thought fea-
ture transformation, selection or reduction of the raw data.
In a batch learning pipeline, preprocessing, fitting and test-
ing a model are distinct phases. They are applied in order,
and the output of this process is a fitted model that can be
used for future data. These same operations are required in
a streaming setting. The main difference is that streaming
data needs the continuous application of the whole pipeline
while it is being used. Consequently, all these phases are
interleaved in an online process, which requires an intricate
orchestration of the pipeline and ideally does not rely on
performing some tasks offline.
Garc´ıa et al. [51] discussed the challenges concerning pre-
processing techniques for big data environments, focusing on
how different big data frameworks, such as Hadoop, Spark,
and Flink deal with them and which methods are imple-
mented for a wide range of preprocessing tasks, including:
Feature selection, instance selection, discretization, and oth-
ers. Ram´ırez-Gallego et al. [103] focus specifically on pre-
processing techniques for data streams, mentioning both ex-
isting methods and open challenges. Critical remarks were
made in [103], such as: the relevance of proposing novel dis-
cretization techniques that do not rely solely on quantiles
and that perform better in the presence of concept drifts;
expanding existing preprocessing techniques that deal with
concept drift to also account for recurrent drifts; and the
need for procedures that address non-conventional problems,
e.g., multi-label classification.
In this section, we focus on issues and techniques that were
not thoroughly discussed in previous works, such as sum-
marization sketches. Other aspects that can be considered
as part of preprocessing, notably dealing with imbalanced
data, and others, are discussed in further sections.
2.1 Feature transformation
2.1.1 Summarization Sketches
Working with limited memory in streaming data is non-
trivial, since data streams produce insurmountable quan-
tities of raw data, which are often not useful as individual
instances, but essential when aggregated. These aggregated
data structures can be used for further analysis such as:
“what is the average age of all customers that visited a given
website in the last hour?” or to train a machine learning
model.
The summary created to avoid storing and maintaining a
large amount of data is often referred to as a ‘sketch’ of
the data. Sketches are probabilistic data structures that
summarize streams of data, such that any two sketches of
individual streams can be combined into the sketch of the
combined stream in a space-efficient way. Using sketches re-
quires a number of compromises: sketches must be created
by using a constrained amount of memory and processing
time, and if the sketches are not accurate, then the informa-
tion derived from them may be misleading.
Over the last decades, many summarization sketches have
been proposed. Ranging from simple membership techniques
such as Bloom filters [22] and counting strategies to more ad-
vanced methods such as CM-Sketch [30], and ADA-Sketches
[112]. Bloom filters are probabilistic data structures used to
test whether an element is a member of a set, using hash
functions. CM-Sketch is essentially an extension of Bloom
filters used to count the frequency of different elements in a
stream.
Sketches are becoming a popular approach to cope with data
streams [4; 108; 128]. In the previous decade, the adop-
tion of sketching for stream processing was taken with some
skepticism, for example, Gaber et al. [45] suggests using di-
mensionality reduction techniques, such as Principal Com-
ponents Analysis, as a more sustainable approach for stream
processing.
Novel sketches have been proposed based on the idea of
building a meta-sketch using several sketches as components.
The Slim-Fat Sketch [128] (SF-Sketch) is an example of this,
that outperforms single sketches.
Sketches can also be used in machine learning methods for
data streams. For example, the Graphical Model Sketch [75]
is a sketch used inside Bayesian networks, or in the Naive
Bayes classifier, to reduce the size of memory used. How
to use sketches inside other machine learning methods is an
open question.
2.1.2 Feature Scaling
Feature scaling consists of transforming the features domain
in a way that they are on a similar scale. Commonly, scal-
ing refers to normalizing, i.e., transform features such that
their mean ˆx= 0 and standard deviation σ= 1. In batch
learning, feature scaling is both an important and an unin-
teresting topic, which is often added to the data transfor-
mation pipeline without much thought of the process. It is
important while fitting learners that rely on gradient descent
(e.g., neural networks) as these will converge faster if fea-
tures are about the same scale; or learners that rely on dis-
tances among instances (e.g. k-means, k-nearest neighbors,
and others) as this prevent one dimension with a wide range
of values dominating others when calculating distances. The
two most popular approaches consist of i) centralizing the
data by subtracting the mean and diving by the standard
deviation; or ii) dividing each value by the range (max -
min).
It is unfeasible to perform feature scale for data streams as
aggregate calculations must be estimated throughout the ex-
ecution. For landmark window approaches there are exact
solutions to incrementally computing the mean and stan-
dard deviation without storing all the points seen so far.
We need to maintain only three numbers: the number of
data points seen so far n, the sum of the data points P(x),
and the sum of the squares of the data points P(x2). These
statistics are easy to compute incrementally. The mean is
given by P(x)
nand the variance is given by P(x2)(P(x)2)/n
n1.
In landmark windows, its easy and fast to maintain ex-
act statistics by storing few numbers. However, in time-
changing streams the adaptation is too slow. To deal with
change, sliding window models are more appropriate. The
problem is that exact computation of the mean or variance
over a stream in a sliding window model requires to store all
data points inside the window. Approximate solutions, us-
ing logarithmic space, are the exponential histograms [31].
Exponential histograms store data in buckets of exponen-
tial growing size: 20,21,22,23, . . .. For a window size W,
only log(W) space is required. Recent data are stored in
fine granularity, while past data are stored in an aggregated
form. The statistics computed using exponential histograms
are approximate, with error bounds. The error comes from
the last bucket, where it is not possible to guarantee all data
is inside the window.
The lack of attention on feature scaling by the data stream
mining research community may be justified by the perva-
siveness of the Hoeffding Tree algorithm [37]. Hoeffding
trees maintain the characteristic of conventional decision
trees of being resilient to variations in the features range
of values. Even if there is not much room for theoretical ad-
vances for data stream feature scaling, practical implemen-
tations would be welcome by the community (see section 7).
The most immediate challenge is to provide efficient imple-
mentation of these feature scaling methods that integrate
with other operators (e.g. drift detection) in the streaming
machine learning pipeline. Finally, another aspect is related
to how some learning algorithms were tested on datasets
where features were scaled in an offline process. This cer-
tainly affects the results obtained (see section 5), and an
online transformation would provide more realistic results.
2.1.3 Feature Discretization
Discretization is a process that divides numeric features into
categorical ones using intervals. Depending on the appli-
cation and predictive model being used, discretization can
bring several benefits, including faster computation time as
discrete variables are usually easier to handle compared to
numeric ones; and decreases the chances of overfitting since
the feature space becomes less complex.
Targeting feature discretization from data streams, a signifi-
cant milestone was the Partition Incremental Discretization
algorithm (PiD) [101]. PiD discretizes numeric features in
two layers. The first layer is responsible for computing a
high number of intervals given the arriving data, while the
second uses the statistics calculated in the first layer to com-
pute equal-frequency partitions.
Webb [122] proposed two different schemes for feature dis-
cretization: Incremental Discretization Algorithm (IDA) and
IDAW (where W stands for windowing). IDA uses quantile-
based discretization on the entire data stream using random
sampling, while IDAW maintains a window of the most re-
cent values for an attribute and discretizes these. IDAW
requires more computational time than IDA since it must
be updated more frequently.
The ChiMerge discretization algorithm [77] store the fea-
tures’ values on a binary search tree, which makes it more
robust to noise in comparison previous methods.
Pfahringer et al. [99] compared a range of discretization
schemes for Hoeffding Trees. Based on empirical evalua-
tions, the Gaussian approximation was indicated as the most
accurate method in terms of accuracy and tree growth.
Finally, similarly to feature scaling, the effort on feature dis-
cretization should target the provisioning of efficient imple-
mentations that integrate with different parts of the stream-
ing process, such as classification systems, drift detection,
and evaluation [40].
2.2 Invalid entries handling
Invalid entries may refer to missing, noise or other issues that
may arise (e.g. unknown formats). Characterizing what is
an invalid entry depends on the problem, algorithms, and
software. For example, categorical features may be deemed
as invalid in many machine learning tools (e.g., scikit-learn),
not because they are inherently wrong, but because the soft-
ware was designed in a way that does not account for them.
Despite technical issues related to invalid entries, the most
well known, and studied, problem is missing data. Krempl
et al. [73] commented on the relevance of this problem and
discussed missing values for the output feature as part of
this discussion. We prefer to include this latter case under
our discussion of semi-supervised learning (see section 3.2)
and solely concentrate on the input data in this section.
To address missing values imputation methods are relatively
standard in batch learning [38]. Imputation methods have
not been thoroughly investigated for streaming data yet.
This is mostly because the techniques often rely on observing
the whole data before imputing the values. These techniques
are ‘feasible’ in batch learning, but not for streaming data.
For example, mean and median imputation will encounter
issues such as: How to estimate the mean and the median
for evolving data? The issues with mean estimation were
previously discussed in section 2.1.2.
An option to avoid aggregation calculations is to apply im-
putation by using a learner. For example, a windowed K-
nearest neighbors can be used, such that the k neighbors
values can be used to infer the value of a missing feature in
a given instance.
2.3 Dimensionality reduction
Dimensionality reduction tackles the retention of patterns
in the input data that are relevant to the learning task. We
report the works and gaps on dimensionality reduction tech-
niques that apply transformations to the input data, e.g.,
Principal Component Analysis (PCA), and Random Pro-
jections; while the next section discusses feature selection
techniques tailored for data streams and their shortcomings.
Mitliagkas et al. [86] introduced a memory-limited approx-
imation of PCA based on sampling and sketches that can
be calculated under reasonable error bounds. Yu et al. [129]
proposed a single-pass randomized PCA method, yet, the
method has been solely evaluated on a single image dataset.
Zhou et al. [133] presented an incremental feature learning
algorithm to determine the optimal model complexity for
online data based on the denoising autoencoder.
Another set of techniques that are important for dimension-
ality reduction are those tailored for text data. A notable
implementation of dimensionality reduction in such scenar-
ios is the hashing tricks provided in Vowpal Wabbit [76].
Hashing tricks facilitate the processing of text data as con-
ventional Bag-of-Words and n-grams are unappealing for
streaming scenarios since the entire lexicon for a domain
must be known before the learning process starts, which
is an assumption that is hardly met in streaming domains,
as out-of-vocabulary words may appear over time. With
the hashing trick, each word (feature) in the original text
data is converted into a key using a hash function, which
is used to increment counters in a reduced dimensionality
space that is later fed to the learner. Such a process has a
vital downside as reverse lookups are not possible, i.e., if one
wants to determine which words are the most important for
predictions.
Finally, Pham et al. [100] proposed an ensemble that com-
bines different random projection techniques (Bernoulli,
Achlioptas, and Gaussian projections) with Hoeffding Trees,
and results have shown that the technique is feasible and
competitive against bagging methods when applied to real-
world data.
Further investigation is necessary for all the techniques dis-
cussed in this section, specifically to investigate the effect
of concept drifts. For instance, most of these methods are
single-pass, yet, they have been applied to datasets in a
batch processing scheme. In real-world streaming scenar-
ios, drifts in the original data will induce changes in the
feature transformation outputs of random projections, so
closer analysis is required to investigate how classifiers be-
have according to such changes.
2.4 Feature selection
Feature selection targets the identification of which features
are relevant to the learning task. In contrast to dimension-
ality reduction techniques, feature selection does not apply
transformations to the data, and thus, the features can still
be interpreted. As a by-product, feature selection is also
known in batch machine learning for potentially improving
the computation time, reducing computation requirements,
and enhancing the generalization rates of classification sys-
tems as these are less prone to overfitting.
Feature selection methods tailored for batch settings require
the entire dataset to determine which features are the most
important according to some goodness-of-fit criterion. Nev-
ertheless, this is a requirement that does not hold in stream-
ing scenarios, as new data becomes available over time. Tar-
geting data streams, Barddal et al. [10] showed that hoeffd-
ing (decision) trees [37] and decision rules [71] are the major
representatives of classification and regression systems that
can incrementally identify which features are the most im-
portant.
Incrementally identifying which features are important is a
relevant subject in data streams. New methods must be de-
signed so that the feature selection process can identify and
adapt to changes in the relevance of features, a phenomenon
called feature drift (see section 4.2).
Another critical gap of feature selection in streaming sce-
narios regards the evaluation of feature selectors. There are
different factors to account for when evaluating feature selec-
tion proposals. Throughout the years, different quantitative
measures, such as accuracy and scalability; and subjective
ones, such as “ease of use”, have been used to highlight
the efficiency of feature selectors [47]. First, it is crucial
to assess the behavior of feature selection algorithms when
combined with different learners, as each learner builds its
predictive model differently despite being fed with the same
subset of features. On the other hand, it is important to
make sure that the feature selection process is accurate, i.e.,
the selected subset of features matches the features that are
indeed relevant [47].
Finally, feature selectors are expected to be “stable”, mean-
ing that they should select the same features despite being
trained with different subsets of data [91]. In batch learn-
ing, stability metrics target the measuring of whether the
selected subset of features across different data samples of
the same distribution match [74]. Stable methods are pre-
ferred as they facilitate learning a model from the data, i.e.,
the subset of features is fixed. However, an open challenge
in the streaming setting is the contradiction between feature
stability and selection accuracy. If the features’ importance
shifts over time (feature drifts) then the feature selection
method will need to compromise either stability or accu-
racy.
3. THE LEARNING PROCESS
Learning from streaming data requires real-time (or near
real-time) updates to a model. There are many important
aspects to consider in this ‘learning phase’, such as deal-
ing with verification latency. In this section, we discuss the
relationship between data streams and time series; the prob-
lem of dealing with partially and delayed labels; ensemble
learning; imbalanced data streams and the essential issue of
detecting anomalies from streaming data.
3.1 Time series
Time series data may commonly arrive in the form of online
data, and it thus can be treated as a data stream. Another
way of seeing it: data streams may often involve temporal
dependence and thus be considered as time series. A com-
parison of data streams and time series methods is given in
ˇ
Zliobaite et al. [121]. It was pointed out that many bench-
mark datasets used in data streams research exhibit time
series elements, as exemplified in Fig. 1.
Figure 1: A small section of the well-known Electricity
dataset; a common benchmark in data stream evaluations.
A time series nature is clearly visible with regard to tempo-
ral dependence, both in the features (plotted in solid lines)
and the class labels (shown above).
Unlike a regular data stream, where instances are assumed
to be independently and identically distributed1(i.i.d.), data
points in a time series are expected to exhibit strong tem-
poral dependence.
Data stream methods can be adapted to such scenarios us-
ing relatively simple strategies, such as aggregating the la-
bels of earlier instances into the instance space of the current
instance [121]. There are special considerations in terms of
evaluation under concept drifting streams, in particular re-
garding the selection of benchmark classifiers (see, again,
ˇ
Zliobaite et al. [121]). Further discussion on evaluation
strategies is given in section 5.
Considering a moving window of instances can be viewed
as removing time dependence, and thus converting a time
series into an ordinary data stream. If P(yt|xt, xt1) =
P(yt|xt) does not hold, it indicates temporal dependence
in the data stream. The idea is to produce a new stream of
instances x0
t:= [xt, xt1,...,xt`] over a window of size `,
sufficient such that P(yt|x0
t) = P(yt|x0
t, x0
t1); thus produc-
ing a temporally-independent (i.e., ‘regular’) data stream.
It is also possible to use a memory device to embed an arbi-
trarily long series into such an instance representation, for
example, by using an echo state network or another kind of
recurrent neural network (see section 6.2 for further consid-
eration in the context of streams). An experimentation of
such an approach in data streams was carried out in [83]
under Hoeffding tree methods.
We can note that the filtering task of sequential state-space
models, such as the hidden Markov model (HMM), are di-
rectly applicable to classification in data streams. Indeed,
one can see HMMs as a sequential version of naive Bayes
(a common data-streams benchmark), simply by modelling;
see Fig. 2 for a graphical intuition. Kalman filters and par-
ticle filters can similarly be considered under the continuous
output (i.e., regression) scenario. See [8; 41] for a compre-
hensive overview of these methods.
We do remark that, unlike in a typical scenario of these mod-
els, learning cannot be done on a full forward-backward pass
1The ‘identically distributed’ assumption may be relaxed in
the presence of concept drift; but in any case we may say
i.i.d. with regrad to a particular concept.
y1y2y3y4
x1x2x3x4
Figure 2: A probabilistic graphical model representation of
a generative model (such as a hidden Markov model, where
temporal dependence is considered; exemplified over four
time points.
(such as using the Baum-Welch algorithm [13]) because in a
stream there is no fixed end to the sequence and predictions
are needed at the current timestep, not retrospectively. It
can, however, be done over a window, and we are not aware
of any work that considers explicitly this in the data-stream
context – an open challenge.
The open challenge relating to data stream learning is to
draw more solid links to the existing time series literature.
On the theoretical level, connections between models in the
respective areas should be formalized, thus clarifying which
time series models are applicable in data streams and in
which contexts. An empirical comparison of methods across
these domains is needed, not only to reveal the extent of
time series nature within standard data sources found in the
time series literature but also indicate on the practical ap-
plicability of methods from the rich and diverse time-series
literature. Undoubtedly, techniques (in particular those of
drift detection), could also be used to enhance time-series
methods.
3.2 Semi-Supervised learning
Semi-supervised learning (SSL) problems are challenging,
appear in a multitude of domains, and are particularly rel-
evant to streaming applications2where data are abundant,
but labeled data may be rare. To address SSL problems,
one can either ignore the unlabeled data and focus on the
labeled data; try to leverage the unlabeled data; or assume
some labels are available per request (active learning). The
first implies a supervised problem; the second relies on find-
ing and exploring a specific characteristic of the data; while
the third depends on an external agent to provide the re-
quired labels on time.
In this section we focus the discussion on the last two op-
tions, leveraging unlabeled data and active learning. Still, it
is essential to consider the first option, supervised learning,
for practical applications as discussed by Oliver et al. [92],
since a robust supervised model trained only on the la-
beled data may outperform intricate semi-supervised meth-
ods. On top of that, active learning might not always be
feasible as labeling the data can be costly financially and in
terms of time.
Even for supervised problems, it is reasonable to assume
that immediately labeled data will not be available. For ex-
ample, in a real-world data stream problem it is often the
case that the algorithm is required to predict xand only af-
ter several time units its true label yis going to be available.
This problem setting leads to a situation where verification
2Also referred to as ‘partially labelled streams’ or ‘infinitely
delayed stream’.
Stream learning
Immediate Delayed Never
(Unsupervised)
Fixed Varying
All labeled
(Supervised)
Some labeled
(Semi-Supervised)
Figure 3: Stream learning according to labels arrival
time [54].
latency, or delay, takes place. One can assume a delayed
labeled stream as an SSL problem and ignore that some (or
all) of the labels will be available at some point in the future.
Therefore delayed labeled streams can be tackled with SSL
techniques. Figure 3 presents a categorization of how super-
vised, semi-supervised and verification latency are related
w.r.t the label arrival. Plasse and Adams [102] introduce
a taxonomy to determine the delay mechanism and magni-
tude; present real-world applications where delayed labels
occur, e.g., credit scoring; notation for the delayed labeling
setting; and how the set of delayed labels can be used to pre-
update the classifier. However, Plasse and Adams [102] do
not introduce a specific evaluation procedure that accounts
for verification latency.
ˇ
Zliobaite [134] raise the critical questions of if and when
it is possible to detect a concept drift from delayed labeled
data. The work is motivated by a large number of real-world
problems in which labels are delayed due to the problem
characteristics. For example, the ground truth for credit
default prediction can only be obtained several months, or
years, after a decision was made. The work also discusses
the relationship between delayed labeling and active learn-
ing. The former concerns when new labels are needed, while
the latter is related to which instances must be labeled to
minimize cost and maximize prediction performance. It was
concluded that both problems are complementary [134].
The SSL techniques for streaming data includes unsuper-
vised learning combined with supervised learning, e.g., clus-
tering the unlabeled data and pseudo-labeling it based on
the clusters and existing labeled data [109; 85; 64]; active
learning [29]; and hybrid approaches [95]. Each of these
approaches makes assumptions about the problem and the
data distribution, but not very often these assumptions are
explicitly discussed.
Active learning is a popular choice for streaming data. Ac-
tive learning promises to reduce the amount of required la-
beled data in supervised learning to achieve a given level of
predictive performance. In simple terms, the algorithm de-
cides which instances should be labeled given some criteria.
There are a few concerns regarding this approach, such that
it presumably assumes that any instance can be labeled,
which may not be true (it depends on the domain); and it
includes an outsider (usually a human) in the learning pro-
cess, i.e., someone who is going to provide the labels required
by the algorithm. Assuming a traditional evolving stream
setting, by the time the label is provided the concept has
already changed or the volume of data to be labeled exceeds
the capabilities of the responsible for labeling. For example,
assuming an algorithm performs well with 5% labeled data,
however, to label 5% of a data stream that generates thou-
sands of instances per day is still a difficult, and costly, task
in a variety of domains.
The amount of literature concerning how to exploit unla-
beled instances and how to deal with verification latency has
increased in the past years. Still, open issues include: how to
effectively evaluate algorithms when labels arrive with delay;
how to deal with out-of-order data [78]; and fundamental
theoretical aspects behind existing SSL methods proposed
for non-stationary problems. On top of that, some strate-
gies developed for batch data have not been thoroughly ex-
plored in a streaming scenario, including multi-view learn-
ing and co-training [23]; and transductive support vector
machines [67; 113]. Finally, transfer learning is a somewhat
popular method to alleviate the problem of few labeled in-
stances [94], and it has not been widely adopted for the
streaming setting yet.
3.3 Ensemble learning
Ensemble learning receives much attention for data stream
learning as ensembles can be integrated with drift detection
algorithms and incorporate dynamic updates, such as se-
lective removal or addition of base models [53]. On top of
that, several issues such as concept evolution, feature evolu-
tion, semi-supervised learning, anomaly detection, are often
approached with an ensemble approach for data streams.
The most common use of ensemble models is to allow recov-
ery from concept drifts. Ensemble models can rely on
reactive or active strategies to cope with concept drift. Re-
active strategies continuously update the ensemble, often
assigning different weights to base models according to their
prediction performance. This weighting function may take
into account the recency of prediction mistakes, such that
correct predictions in the latest instances receive a higher
weight in comparison to correct predictions in oldest in-
stances. Examples of these strategies include the Streaming
Ensemble Algorithm (SEA) [116] and Dynamic Weighted
Ensemble (DWM) [70].
A canonical example of an active strategy is ADWIN
Bagging, i.e., the combination of ADWIN [17] and online
bagging [93]. In ADWIN Bagging, each base model clas-
sification output is monitored by an ADWIN instance and
whenever a drift is flagged the corresponding model is reset.
Recently, ensembles that combine reactive and active strate-
gies have been proposed in the literature. Examples include
the Adaptive Random Forest (ARF) [54] and the Streaming
Random Subspaces (SRP) [57], which are adaptations of the
Random Forest [24] and Random Patches algorithms [82],
respectively, to streaming data with the addition of drift
detectors and weighting functions based on models predic-
tion performance. The main difference between ARF and
SRP is that ARF is based on local subspace randomization
and SRP uses a global subspace randomization strategy. In
[57] authors showed that the global subspace strategy is a
more flexible model, which increases diversity among base
models and the overall accuracy of the ensemble.
An ensemble-based method is often used along with other
techniques to address concept evolution. Even though
the ensemble may not be used directly to detect the novel
classes, it is useful to dynamically incorporate the novel
class instances into the whole learning model without major
changes to the already learned models, i.e., other ensem-
ble members. For instance, ensemble approaches combined
with One-Versus-All (OVA) approach to address concept
evolution includes OVA Decision Trees [63], Learn++.NC
and Learn++.UDNC [35].
Ensemble strategies have been used to address feature drift
problems by removing/adding the influence of specific fea-
tures by using single-feature classifiers, such that if a fea-
ture disappears or is identified as irrelevant, its influence
can be wholly removed from the whole system by removing
the classifier associated with it. This approach is similar
to that mentioned previously to cope with concept evolu-
tion (one classifier per class), and it is the approach used
in HSMiner [96], with the addition of using different classi-
fiers according to the feature domain. On top of that, us-
ing single-feature classifiers, or a limited size of features per
learner that improves the algorithm’s scalability as its pro-
cessing can be distributed among multiple machines using a
map-reduce approach [61].
The flexibility that an ensemble strategy allows (i.e., add
and remove models) make it an attractive strategy to deal
with partially labeled (i.e., semi-supervised) streams.
An example is the SluiceBox AnyMeans (SluiceBoxAM) al-
gorithm [97]. SluiceBoxAM is based on the SluiceBox al-
gorithm [95], which already combined different methods to
address problems, such as multi-domain features, concept
drift, novel class detection, and feature evolution. Besides
using a clustering method (AnyMeans) capable of discov-
ering non-spherical clusters SluiceBoxAM can be used with
other ensemble classifiers, e.g., Parker and Khan [97] re-
port the performance of SluiceBoxAM combined with the
leveraging bagging algorithm [21]. The overall idea behind
SliceBoxAM and other ensemble methods combined with
clustering methods.
Open issues related to the deployment of ensemble methods
to practical streaming data include overly complex mod-
els and massive computational resources demands. Some
algorithms are too complex as they contain different learn-
ing strategies and heuristics to cope with various problems.
While addressing different problems is a good trait, these
models are often too complicated for a framework developer
to understand all of their idiosyncrasies, which make them
less attractive to be implemented and deployed in practice.
On top of that, as discussed by Gomes et al. [53] the combi-
nation of too many heuristics raises the question: “Does the
ensemble perform well because of the combination of all its
methods, or simply because some of them are very effective,
while others are effectively useless or even harmful?”
To address these questions, it is important to present in-
depth analysis of how each of the strategies embedded into
the ensemble behaves individually and in combination with
the others. This requires creating experiments that are be-
yond measuring the overall ensemble classification perfor-
mance. In general, it is difficult to isolate aspects of the
method for comparisons, but it is worthwhile to verify if it
is possible when proposing a novel method, especially if it
lacks theoretical guarantees.
The computational resources used by a machine learning
algorithm developed for data streams are of critical impor-
tance. An accurate, yet inefficient method might not be
fit for use in environments with strictly limited resources.
Some ensemble algorithms approach this problem by remov-
ing redundant models when their current predictions are
too similar [55; 56], or their coverage overlaps during train-
ing [109]. These techniques may not enhance the ensem-
ble performance from the learning performance perspective;
in fact they might negatively impact it. Algorithms that
solve more problems are often more challenging to manage,
for example, algorithms that combine clustering and ensem-
bles to address partially labeled streams. One approach is
to distribute the computation using multiple threads [54].
However, there are limits to what can be accomplished with
algorithms executed in a single machine, even if they are
multi-threaded. As a consequence, the machine learning
community is investing efforts into scalable and distributed
systems.
The challenge is how to maintain the characteristics of the
ensemble methods and efficiently distributed them over sev-
eral machines. Some ensemble methods are straightforward
to be adapted to distributed environments (e.g., bagging),
while others are more complicated (e.g., random forests).
Efforts have been driven towards integrated platforms for
stream learning in this context, which resulted in frame-
works (or libraries) such as Apache Scalable Advanced Mas-
sive Online Analysis (SAMOA) [33] and StreamDM.
Currently, there are efforts in deploying stream learning al-
gorithms (ensembles included) in a distributed setting. Ex-
amples include the Streaming Parallel Decision Tree [14],
HSMiner [61] and Vertical Hoeffding Tree (VHT) [72]. En-
sembles are attractive techniques, as discussed in this sec-
tion, and they are probably going to play an essential role in
stream processing software, such as Apache Spark Stream-
ing [130] and Apache Flink [25].
3.4 Imbalanced Learning
Imbalanced datasets are characterized by one class outnum-
bering the instances of the other one [80]. The later is re-
ferred to as the minority class, while the former is identified
as the majority class. These concepts can be generalized
to multi-class classification and other learning tasks, e.g.,
regression [118]. The imbalance may be inherent to the
problem (intrinsic) or caused by some fault in the data ac-
quisition (extrinsic). Learning from imbalanced datasets is
challenging as most learning algorithms are designed to op-
timize for generalization, and as a consequence, the minority
class may be completely ignored.
The approaches for dealing with imbalanced datasets com-
monly rely on cost-sensitive learning;resampling meth-
ods (oversampling and undersampling); and ensemble learn-
ing. Cost-sensitive strategies rely on assigning different
weights to incorrect predictions. This can be used to in-
crease the cost of a minority class error, which shall ‘bias’
the learning process in its favor. Resampling methods rely
on removing instances from the majority class (undersam-
pling) or creating synthetic instances for the minority class
(oversampling). These methods tend to be costly even for
batch learning, as in general, they require multiple distance
computations among training instances, e.g., SMOTE [26].
Finally, ensemble strategies for imbalanced learning use the
ensemble structure alongside cost-sensitive learning or re-
sampling strategies [46].
Besides the issues related to the dataset imbalance, in a
streaming scenario, other challenges may arise. For exam-
ple, given two classes labels which distribution is balanced,
for a given period one of them may be underrepresented;
thus leading to a ‘temporary’ imbalance. Another possibil-
ity is that the class distribution variations indicate a con-
cept drift (e.g., a period of transition in a gradual drift) or
perhaps a concept evolution (e.g., one of the classes is dis-
appearing). How to differentiate between these situations
and propose general strategies to address them is still an
open issue. This motivates the development of methods
to address imbalanced streaming data; examples include:
Learn++.NSE [42]; SMOTE [36]; REA [27]; and an adapted
Neural Network [52]. Finally, another challenge is how to de-
velop algorithms that are effective in addressing the imbal-
ance problem, without compromising the computational re-
sources. To this end, the cost-sensitive and ensemble strate-
gies seems to be more effectively than the resampling strate-
gies (specially oversampling).
3.5 Anomaly detection
A significant task in imbalanced learning is that of anomaly
detection. Supervised anomaly detection, where labeled nor-
mal examples and anomalies are part of the dataset, is indis-
tinguishable from a standard imbalanced learning problem
– where the anomalous instances belong to the underrepre-
sented (minority) class. However, in most practical scenar-
ios, it is not feasible to get verified labels, particularly for
non-stationary data streams. Therefore, in a real scenario,
one might need to choose between unsupervised learning and
semi-supervised learning.
For the sake of generality, many methods assume that no
labeled data is available, and the problem is tackled essen-
tially as a clustering or density-estimation task. In this case,
instances ‘too far’ from the centers of established clusters,
or densities, are considered anomalies. Existing clustering
algorithms for streaming data, such as CluStream [3] can
be used for this purpose. However, a challenge is that some
of these methods rely on an offline step where the actual
clustering method, e.g., k-means, is executed. The online
step is responsible only for updates to the data structures
that summarize the incoming data. Salehi and Rashidi [110]
presents a recent survey on anomaly detection for evolving
data, with a particular focus on unsupervised learning ap-
proaches.
In some scenarios, a small set of labeled normal instances
and anomalies is available. This case can neither be charac-
terized as supervised nor unsupervised, but as semi-supervised
learning (see section 3.2). In a semi-supervised anomaly de-
tection it is assumed that some normal examples and anoma-
lies will not be labeled, but besides that, some anomalies
might not even be known beforehand, while others might
cease to exist altogether. The critical aspect in such sce-
nario is the evolution of the labels overtime. This can take
the form of adversarial machine learning [65], where
an adversarial opponent actively attempt to jeopardize the
learning model using several types of attacks3. Furthermore,
a problem where labels appear and disappear overtime can
be formulated as an evolving concept (or novel class detec-
tion) problem [88] (see section 4).
We highlight the need for further discussion around the in-
tersection among anomaly detection, adversarial learning,
semi-supervised learning, and novel class detection. Finally,
some of the latest proposed algorithms for data stream anomaly
3Barreno et al. [12] presents a taxonomy of such attacks.
detection are RS-Forest [125], Robust Random Cut Forest
Based [59], Threaded Ensemble of Autoencoders [39], and
OnCAD [28].
4. REASONING ABOUT THE LEARNING
PROCESS
Learning from data streams is a continuous process. The
learning systems that act in dynamic environments, where
working conditions change and evolve, need to monitor their
working conditions. They need to monitor the learning
process for change detection, emergence of novel classes,
changes in the relevance of features, changes in the optimal
parameters settings, and others. The goal of this section
is to discuss the design aspects of learning systems that can
monitor their performance. In a general sense, these learning
systems should be able of self-diagnosis when performance
degrades, by identifying the possible causes of degradation
and self-repairing or self-reconfiguring to recover to a stable
status.
Much research has been devoted to characterizing concept
drift [123], detecting and recovering from it [50], recurrent
concepts [48; 49]. Since this is a frequent topic when dis-
cussing learning from data streams, we refrain from review-
ing it entirely and focus mostly on current issues related to
it, such as feature drifts and their relationship to feature se-
lection; drift detection under unsupervised/semi-supervised
and delayed labeled scenarios; and hyper-parameter tuning.
4.1 Concept drift and label availability
Novel concept drift detection algorithms are proposed each
year, and their performance assessed using different meth-
ods (see section 5). Most of these algorithms are applied
to the univariate stream of correct/incorrect predictions of
a learner. To achieve detections in a timely fashion, this
requires that the ground-truth be available almost imme-
diately after the prediction is made. This ‘immediate’ set-
ting can be characterized by the ground-truth ytof instance
xtbeing available before the next instance xt+1 is avail-
able (see section 3.2). Algorithms such as ADWIN [17] and
EDDM [7], were tested under the aforementioned assump-
tion. However, if the ground-truth is not immediately avail-
able, then these algorithms’ ability to detect drifts might be
severely decreased.
Algorithms focusing on drift detection on delayed or par-
tially labeled streams exists. Examples include SUN [126]
and the method from Klinkenberg [69] based on support
vector machines. The former uses a clustering algorithm to
produce ‘concept clusters’ at leaves of an incremental deci-
sion tree, and drifts are identified according to the deviation
between history concept clusters and the current clusters.
ˇ
Zliobaite [134] presents an analytical view of the conditions
that must be met to allow concept drift detection in a de-
layed labeled setting. Three types of concept drifts are ana-
lytically studied and two of them also empirically evaluated.
Unfortunately, one of the least investigated cases, when the
change occurs in the input data distribution, was not em-
pirically investigated. Therefore, the proposed methods to
detect changes in the input data, such as parametric and
non-parametric multivariate two-sample tests, were not dis-
cussed in-depth. We further address the problem of identi-
fying changes in the input data distribution in section 4.2.
Figure 4: Two features IG variation over time for SPAM
CORPUS. Adapted from [9].
4.2 Feature drift
Data streams are subject to different types of concept drifts.
Examples include (i) changes in the values of a feature and
their association with the class, (ii) changes in the domain
of features, (iii) changes in the subset of features that are
used to label an instance, and so on.
Despite considered in seminal works of the area [124], only
recently works on the above-emphasized type of drift have
gained traction. A feature drift occurs when a subset of fea-
tures becomes, or ceases to be, relevant to the learning task
[10]. Following the definition provided by Zhao et al. [132],
a feature xiis deemed relevant if Si=X\ {xi}, S 0
iSi,
such that P(Y|xi, S0
i)> P (Y|S0
i) holds; and irrelevant oth-
erwise. Given the previous definition, the removal of a rel-
evant feature decreases the prediction power of a classifier.
Also, there are two possibilities for a feature to be relevant:
(i) it is strongly correlated with the class, or (ii) it forms a
subset with other features, and this subset is correlated with
the class [132]. An example of features importance varying
over time can be visualized in Fig. 4, where the Information
Gain for two features, w.r.t the target variable, is plotted
over time for the SPAM CORPUS [68] dataset.
As in conventional drifts, changes in the relevant subset of
features affect the class-conditional probabilities P(Y|X) as
the decision boundary across classes changes. Therefore,
streaming algorithms should be able to detect these changes,
enabling the learning algorithm to focus on the relevant fea-
tures, leading to lighter-weighted and less overfit models.
To address feature drifts, few proposals have been presented
in the literature. Barddal et al. [10] showed that Hoeffding
Adaptive Trees [18] are the state-of-the-art learners for iden-
tifying changes in the relevance of features and adapting the
model on the fly. Another important work that explicitly fo-
cuses on performing feature selection during the progress of
data streams was HEFT-Stream [90], where new features are
selected as new batches of arriving data become available for
training.
Finally, the assessment of feature drifting scenarios should
not only account for the accuracy rates of learners, but
also whether the feature selection process correctly flags the
changes in the relevant subset of features and if it identifies
the features it should. Given that, the evaluation of feature
selectors should also be dynamic, as the ground-truth subset
of relevant features may drift over time.
4.3 Feature evolution
Another important trait of streaming scenarios regards the
appearance and disappearance of features over time. In
practice, if a new feature becomes available over time, and
if it deemed relevant, one may argue that a feature drift has
occurred, and then this feature could be incorporated into
the learning process. Similarly, if a feature becomes un-
available, then all of its values might be treated as missing
values, and then the learning model should ignore its ex-
istence. Most of the existing frameworks we will discuss in
section 7, e.g., MOA [20], SAMOA [33], Scikit-multiflow [87],
do not account for changes in the input vector of streaming
data. Therefore, in dynamic scenarios where features may
appear and disappear over time, the data stream computa-
tional representation in these frameworks will either remain
static or require external updates. Developing an efficient
dynamic input vector representation for streaming data is
an important and difficult task. Given its relevance to some
problem domains it deserves attention from machine learn-
ing framework developers.
4.4 Concept evolution
Concept evolution is a problem intrinsically related to oth-
ers, such as anomaly detection for streaming data [43]. In
general terms, concept evolution refers to the appearance
or disappearance of class labels. This is natural in some
domains, for example, the interest of users in news media
change over time, with new topics appearing and older ones
disappearing. Another example is Intrusion Detection Sys-
tems, where new threats appear as attackers evolve. Ideally,
these threats must first be identified and then used for im-
proving the model, however doing it automatically is a dif-
ficult task. Informally, the challenge is to discern between
concept drifts, noise and the formation of a novel concept.
Examples of algorithms that address concept evolution in-
cludes: ECSMiner [84], CLAM [5], and MINAS [32].
A major challenge here is the definition of evaluation setup
and metrics to assess algorithms that detect concept evolu-
tion.
4.5 Hyperparameter tuning for evolving data
streams
Hyperparameter tuning (or optimization) is often treated
as a manual task where experienced users define a subset of
hyperparameters and their corresponding range of possible
values to be tested exhaustively (Grid Search), randomly
(Random Search) or according to some other criteria [11].
The brute force approach of trying all possible combinations
of hyperparameters and their values is time-consuming but
can be efficiently executed in parallel in a batch setting.
However, it can be difficult to emulate this approach in an
evolving streaming scenario. A naive approach is to sepa-
rate an initial set of instances from the first instances seen
and perform an offline tuning of the model hyperparame-
ters on them. Nevertheless, this makes a strong assumption
that even if the concept drifts the selected hyperparameters’
values will remain optimal. The challenge is to design an ap-
proach that incorporate the hyperparameter tuning as part
of the continual learning process, which might involve data
preprocessing, drift detection, drift recovery, and others.
Losing et al. [81] present a review and comparison of in-
cremental learners including SVM variations, tree ensem-
bles, instance-based models and others. Interestingly, this
is one of the first works to benchmark incremental learn-
ers using a strategy to perform hyperparameter optimiza-
tion. To perform the tuning a minimum of 20% (or 1,000
instances, whichever is reached first) of the training data
was gathered. Assuming a stationary distribution, this ap-
proach performs reasonably well. Experiments with non-
stationary streams are also briefly covered by the authors,
but since the algorithms used were not designed for this set-
ting, it was not possible to draw further conclusions about
the efficiency of performing hyperparameter optimization on
drifting streams.
A recent work, [120] formulates the problem of parameter
tuning as an optimization problem. It uses the Nelder-Mead
algorithm to exploit the space of the parameters. The Nel-
derMead method [89] or downhill simplex method is a nu-
merical method used to find the minimum or maximum of
a function in a multidimensional space.
5. EVALUATION PROCEDURES AND DATA
SOURCES
As the field evolves and practitioners, besides researchers,
also start to apply the methods, it is critical to verify whether
or not the currently established evaluation metrics and bench-
mark datasets fit the real world problems. The importance
of selecting appropriate benchmark data is to avoid making
assumptions about algorithms quality given empirical tests
on data that might not reflect realistic scenarios.
Existing evaluation frameworks address issues such as im-
balanced data, temporal dependences, cross-validation, and
others [16]. For example, when the input data stream ex-
hibit temporal dependences, a useful benchmark model is a
naive No Change learner. This learner always predicts the
next label as the previous label and, surprisingly, it may
surpass advanced learning algorithms that build complex
models from the input data. ˇ
Zliobaite et al. [135] propose
the κ-temporal statistic, which incorporates the No Change
learner to the evaluation metric.
However, one crucial issue related to the evaluation of stream-
ing algorithms is the lack of appropriate approaches to eval-
uate delayed labeled problems. As previously discussed (see
section 3.2) in a delayed setting there is a non-negligible de-
lay between the input data xand the ground-truth label y,
which can vary from a few minutes/hours up to years de-
pending on the application. A naive approach to evaluating
the quality of such solutions is to record the learner predic-
tion ˆywhen xis presented and then compare it against y
once it is available. One issue with this approach is that in
some applications, such as peer-to-peer lending and airlines
delay prediction, the learner will be pooled several times
with the same xbefore yis available, potentially improving
its performance as time goes by as other labels are made
available and used to update the model. Ideally, the learner
should be capable of outputting better results since the first
prediction when xis presented, however how to measure its
ability to improve over time before yis made available? De-
spite works that address delayed labeling, evaluation frame-
works have only recently been proposed [58].
5.1 Benchmark data
Data stream algorithms are usually assessed using a bench-
mark that is a combination of synthetic generators and real-
world datasets. The synthetic data is used to allow showing
how the method performs given specific problems (e.g., con-
cept drifts, concept evolution, feature drifts, and so forth) in
a controlled environment. The real world datasets are used
to justify the application of the method beyond hypothetical
situations; however, they are often used without guarantees
that the issues addressed by the algorithm are present. For
example, it is difficult to check if and when a concept drift
takes place in a real dataset. The problem is that some of
the synthetic streams can be considered too straightforward
and perhaps outdated, e.g., STAGGER [111] and SEA [116]
datasets.
When it comes to real-world data streams, some researchers
use datasets that do not represent data streams or that are
synthetic data masquerade as real datasets. An example
that covers both concerns (not a stream and actually syn-
thetic) is the dataset named Pokerhand4, at some point in
past it was used to assess the performance of streaming clas-
sifiers, probably because of its volume. However, it is neither
“real” nor a representation of a stream of data. Until today
it is still in use without any reasonable explanation. Even
benchmark datasets that can be interpreted as actual data
streams display some unrealistic characteristics that are of-
ten not discussed. Electricity [62] depicts the energy market
from the Australian New South Wales Electricity Market,
and even though the data was generated over time, often
the dataset version used was preprocessed in an offline pro-
cess to normalize the features, which might benefit some
algorithms or at least ‘leak’ some future characteristics (see
section 2.1.2).
Issues with evaluation frameworks are not limited to su-
pervised learning in a streaming context. For instance, as-
sessing concept drift detection algorithms is also subject to
controversies. A standard approach to evaluate novel con-
cept drift detection is to combine them with a classification
algorithm and assess the detection capabilities of the con-
cept drift method indirectly by observing the classification
performance of the accompanying algorithm. The problem
with this evaluation is that it is indirect; thus the actual
characteristics of the drift detection algorithm, such as the
lag between drift and detection, cannot be observed from it.
This issue is detailed in a recent work [15].
Why are we not using real data streams to assess the per-
formance of stream learning algorithms? One possible an-
swer is the difficulty in preparing sensor data. Even though
the data is abundant, it is still necessary to transform it to
a suitable format, and often this means converting from a
multivariate time series (see section 3.1) to a data stream.
Another possible answer is that realistic data stream sources
can be complicated to configure and to replicate.
5.2 Handling real streaming data
For actual implementations, an important aspect of stream-
ing data is that the way the data is made available to the
system is significant. High latency data sources will ‘hold’
the whole system, and there is nothing the learning algo-
rithm can do to solve it. Different from batch learning, the
4https://archive.ics.uci.edu/ml/datasets/Poker+
Hand
data source for streaming data is often harder to grasp for
beginners. It is not merely a self-contained file or a well-
defined database, and in fact, it has to allow the appear-
ance of new data with low latency in a way that the learner
is updated as soon as possible when new data is available.
At the vanguard of stream processing there are frameworks,
such as Apache Spark and Flink.
Recently, the team behind Apache Spark introduced a novel
API to handle streaming data, namely the Structured Stream-
ing API [6], which overshadows the previous Spark Stream-
ing [130] API. Similar to its predecessor, Structured Stream-
ing is mainly based on micro-batches, i.e., instead of imme-
diately presenting new rows of data to the user code, the
rows are combined into small logical batches. This facilitates
manipulating the data as truly incremental systems can be
both difficult to the user to handle and to the framework de-
veloper to come up with efficient implementations. Besides
implementation details, the main difference between Spark
Streaming and the new Structured Streaming API is that
the latter assumes that there is a structure to the streaming
data, which make it possible to manipulate data using SQL
and uses the abstraction of an unbounded table.
6. STREAMING DATA IN ARTIFICIAL IN-
TELLIGENCE
In this section, we look at stream mining in recent advanced
topics in artificial intelligence, by which we mean tasks that
fall outside of the traditional single-target classification or
regression scenario.
6.1 Prediction of Structured Outputs
In structured output prediction, values for multiple target
variables are predicted simultaneously (for each instance).
A particular well-known special case is that of multi-label
classification [106; 34; 131] where multiple labels are associ-
ated with each data point – a natural point of departure for
many text and image labeling tasks.
Methods can be applied directly in a ‘problem transfor-
mation’ scenario or adapted in an ‘algorithm adaptation’
scheme [104], however, obtaining scalable models is inher-
ently more challenging, since the output space is KLfor L
label variables each taking Kvalues, as opposed to Kfor a
K-class (single-label) problem.
In other words: the output space may be of the same range
of variety and dimensionality as an input space. As such we
can consider the issues and techniques outlined in sections
4.2 and 4.3.
We can emphasize that in multi-output data streams there
is an additional complication involving concept drift which
now covers an additional dimension – models are inherently
more complex and more difficult to learn and thus there is
even greater motivation to adapt them as best as possible
to the new concept when drift occurs, rather than resetting
them. This is further encouraged under the consideration
that supervised labeling is less likely to be complete under
this scenario.
Structured-output learning is the case of multi-output learn-
ing where some structure is assumed in the problem domain.
For example, in an image, segmentation local dependence is
assumed among pixel variables, and in modeling sequences,
it is often assumed temporal dependence among each of the
output variables. However, essentially all multi-label and
multi-output problems will have some underlying structure
and thus are in fact structured-output problems in the strict
sense. Indeed, many sequence prediction and time-series
models can be applied practically as-is to multi-label prob-
lems and vice-versa [105]. This could include recurrent neu-
ral networks, as we review in section 6.2, or the methods
mentioned already in section 3.1.
Therefore, the main challenges are dealing with the inher-
ently more complex models and drift patterns streams deal-
ing with structured outputs. Complex structured output
prediction tasks such as captioning have yet to be approached
in a data-stream context.
6.2 Recurrent Neural Networks
Many structured-ouput approaches can be approached with
recurrent neural networks (RNNs). These are inherently ro-
bust and well suited to dealing with sequential data, particu-
larly text (natural language) and signals with high temporal
dependence. See, e.g., Du and Swamy [41] present a detailed
overview.
RNNs are notoriously difficult to train. Obtaining good re-
sults on batch data can already require exhaustive experi-
mentation of parameter settings, not easily affordable in the
streaming context. Long Short-Term Memory neural net-
works (LSTMs) have gained recent popularity, but are still
challenging to train on many tasks.
There are simplified RNNs which have been very effective,
such as Time Delay Neural Networks, which simply include
a window of input as individual instances; considered, for
example, in ˇ
Zliobaite et al. [121] in the context of streams.
Another useful variety of RNN more suited to data streams
is the Echo State Networks (ESNs). The weights of the hid-
den layer of an ESN are randomly initialized and not trained.
Only the output layer (usually a linear one) is trained; and
stochastic gradient descent will suffice in many contexts.
ESNs are an interesting way to embed signals into vectors
– making them a good starting point for converting a time
series into an i.i.d. data stream which can be processed by
traditional methods (see also discussion in section 3.1).
RNNs are naturally deployed in a streaming scenario for
prediction, but training them under the context of concept
drift has, to the best of our knowledge, not been widely
approached.
Finally, we could remark that neuro-evolution is popular
as a training method for RNNs in some areas, such as rein-
forcement learning, in particular in policy search approaches
(where the policy map is represented as a neural network);
see, for example, Stanley and Miikkulainen [114]. The struc-
ture of the network is evolved over time (rather than back-
ward propagation of errors), and hence is arguably a more
intuitive option in online tasks.
As training options become easier, we expect RNNs to be a
more common option as a data-streams method.
6.3 Reinforcement learning
Reinforcement learning is inherently a task of learning from
data streams. Observations arrive on a time-step basis, in
a stream, and are typically treated either on an episode ba-
sis (here we can make an analogy with batch-incremental
methods) or on a time-step basis (i.e., instance-incremental
streaming). For a complete introduction to the topic, see,
for example, Sutton and Barto [117].
Figure 5: The Mountain Car problem is a typical benchmark
in reinforcement learning. The goal is to drive the car to the
top. It can be treated as a streaming problem.
The goal in reinforcement learning is to learn a policy, which
is essentially a mapping from inputs (i.e., observations) to
outputs (i.e., actions). This mapping is conceptually simi-
lar to that desired from a machine learning model (mapping
inputs to outputs). However, the peculiarity is that ground-
truth training pairs are never presented to the model, unlike
in the typical supervised learning scenario. Rather a reward
signal is given instead of true labels. The reward signal is of-
ten sparse across time and – the most significant challenge –
is that the reward at a particular time step may correspond
to an action taken many time steps ago, and it is thus dif-
ficult to break down into a time-step basis. Nevertheless,
in certain environments, it is possible to consider training
pairs on an episode level.
Despite the similarities with data-streams, there has been
relatively little overlap in the literature. It is not difficult to
conceive of scenarios where a reinforcement-learning agent
needs to detect concept drift in its environment, just as any
classification or regression model.
Reinforcement learning is still in its infancy relative to tasks
such as supervised classification – especially in terms of in-
dustrial applications, which may explain the lack of consid-
eration of additional complications typically considered in
data-streams, as concept drift. Nevertheless, we would ex-
pect such overlap to increase as a wider variety of real-world
application domains are considered.
7. SOFTWARE PACKAGES AND FRAME-
WORKS
In this section, we present existing tools for applying ma-
chine learning to data streams for both research and prac-
tical applications. Initially, frameworks were designed to
facilitate collaboration among research groups and allow re-
searchers to test their ideas directly. Nowadays, tools such
as the Massive Online Analysis (MOA) [20] can be adapted
to deploy models in practice depending on the problem re-
quirements.
Massive Online Analysis (MOA)5[20]. The MOA frame-
work includes several algorithms for multiple tasks concern-
ing data stream analysis. It features a larger community of
researchers that continuously add new algorithms and tasks
to it. The current tasks included in MOA are classifica-
tion, regression, multi-label, multi-target, clustering, outlier
detection, concept drift detection, active learning, and oth-
5http://moa.cms.waikato.ac.nz
ers. Besides learning algorithms, MOA also provides: data
generators (e.g., AGRAWAL, Random Tree Generator, and
SEA); evaluation methods (e.g., periodic holdout, test-then-
train, prequential); and statistics (CPU time, RAM-hours,
Kappa). MOA can be used through a GUI (Graphical User
Interface) or via command line, which facilitates running
batches of tests. The implementation is in Java and shares
many characteristics with the WEKA framework [60], such
as allowing users to extend the framework by inheriting ab-
stract classes. Very often researchers make their source code
available as an MOA extension6.
Advanced Data mining And Machine learning Sys-
tem (ADAMS)7[107]. ADAMS is a workflow engine de-
signed to prototype and maintain complex knowledge work-
flows. ADAMS is not a data stream learning tool per se,
but it can be combined with MOA, and other frameworks
like SAMOA and WEKA, to perform data stream analysis.
Scalable Advanced Massive Online Analysis (SAMOA)8
[33]. SAMOA combines stream mining and distributed com-
puting (i.e., MapReduce), and is described as a framework
as well as a library. As a framework, SAMOA allows users to
abstract the underlying stream processing execution engine
and focus on the learning problem at hand. Currently, it
is possible to use Storm (http://storm.apache.org) and
Samza (http://samza.incubator.apache.org). SAMOA
provides adapted versions of stream learners for distributed
processing, including the Vertical Hoeffding Tree algorithm
[72], bagging and boosting.
Vowpal Wabbit (VW)9. VW is an open source machine
learning library with an efficient and scalable implementa-
tion that includes several learning algorithms. VW has been
used to learn from a terafeature dataset using 1000 nodes in
approximately an hour [2].
StreamDM10. StreamDM is an open source framework for
big data stream mining that uses the Spark Streaming [130]
extension of the core Spark API. One advantage of StreamDM
in comparison to existing frameworks is that it directly ben-
efits from the Spark Streaming API, which handles much of
the complex problems of the underlying data sources, such
as out of order data and recovery from failures.
Scikit-multiflow11 [87]. Scikit-multiflow is an extension
to the popular scikit-learn [98] inspired by the MOA frame-
work. It is designed to accommodate multi-label, multi-
output and single output data stream mining algorithms.
One advantage of scikit-multiflow is its API similarity to
scikit-learn, which is widely used worldwide.
Ray RLlib12 [79]. RLlib is a reinforcement learning li-
brary that features reference algorithms’ implementations
and facilitates the creation of new algorithms through a set
of scalable primitives. RLlib is part of the open source Ray
project. Ray is a high-performance distributed execution
framework that allows Python tasks to be distributed across
larger clusters.
6http://moa.cms.waikato.ac.nz/moa-extensions/
7https://adams.cms.waikato.ac.nz
8http://samoa.incubator.apache.org
9https://github.com/VowpalWabbit/vowpal_wabbit
10http://huawei-noah.github.io/streamDM/
11https://github.com/scikit-multiflow/
scikit-multiflow
12https://ray.readthedocs.io/en/latest/rllib.html
8. CONCLUSIONS
We have discussed several challenges that pertain machine
learning for streaming data. In some cases, these challenges
have been addressed (often partially) by existing research,
which we discuss and point out the shortcomings. All the
topics covered in this work are important, but some have
a broader impact or have been less investigated. Further
developing these in the near future will help the development
of the field:
Explore the relationships between other AI develop-
ments (e.g., recurrent neural networks, reinforcement
learning, etc.) and adaptive stream mining algorithms;
Characterize and detect drifts in the absence of imme-
diately labeled data;
Develop adaptive learning methods that take into ac-
count verification latency;
Incorporate pre-processing techniques that continuously
transform the raw data;
It is also important to develop software that allows the ap-
plication of data stream mining techniques in practice. In
recent years, many frameworks were proposed, and they
are constantly being updated and maintained by the com-
munity. Finally, it is unfeasible to cover all topics related
to machine learning and streaming data in a single paper.
Therefore, we were able to only scratch the surface for some
topics that deserve further analysis in the future, such as
regression; unsupervised learning; evolving graph data; im-
age, text and other non-structured data sources; and pattern
mining.
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