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Learning from crowdsourced labeled data: a survey

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With the rapid growing of crowdsourcing systems, quite a few applications based on a supervised learning paradigm can easily obtain massive labeled data at a relatively low cost. However, due to the variable uncertainty of crowdsourced labelers, learning procedures face great challenges. Thus, improving the qualities of labels and learning models plays a key role in learning from the crowdsourced labeled data. In this survey, we first introduce the basic concepts of the qualities of labels and learning models. Then, by reviewing recently proposed models and algorithms on ground truth inference and learning models, we analyze connections and distinctions among these techniques as well as clarify the level of the progress of related researches. In order to facilitate the studies in this field, we also introduce open accessible real-world data sets collected from crowdsourcing systems and open source libraries and tools. Finally, some potential issues for future studies are discussed.
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Artif Intell Rev
DOI 10.1007/s10462-016-9491-9
Learning from crowdsourced labeled data: a survey
Jing Zhang1·Xindong Wu2·Victor S. Sheng3,4
© Springer Science+Business Media Dordrecht 2016
Abstract With the rapid growing of crowdsourcing systems, quite a few applications based
on a supervised learning paradigm can easily obtain massive labeled data at a relatively low
cost. However, due to the variable uncertainty of crowdsourced labelers, learning procedures
face great challenges. Thus, improving the qualities of labels and learning models plays a
key role in learning from the crowdsourced labeled data. In this survey, we first introduce the
basic concepts of the qualities of labels and learning models. Then, by reviewing recently
proposed models and algorithms on ground truth inference and learning models, we ana-
lyze connections and distinctions among these techniques as well as clarify the level of the
progress of related researches. In order to facilitate the studies in this field, we also introduce
open accessible real-world data sets collected from crowdsourcing systems and open source
libraries and tools. Finally, some potential issues for future studies are discussed.
This research has been supported by the China Postdoctoral Science Foundation under grant 2016M590457,
the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry
of Education, China, under grant IRT13059, the National 973 Program of China under grant
2013CB329604, and the US National Science Foundation under grant IIS-1115417.
BJing Zhang
Xindong Wu
Victor S. Sheng
1School of Computer Science and Engineering, Nanjing University of Science and Technology,
Nanjing 210094, People’s Republic of China
2School of Computer Science and Information Engineering,
Hefei University of Technology, Hefei 230009, People’s Republic of China
3Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA
4Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and
Technology, Nanjing, People’s Republic of China
J. Zhang et al.
Keywords Crowdsourcing ·Learning from crowds ·Multiple noisy labeling ·Label
quality ·Learning model quality ·Ground truth inference
1 Introduction
With the emergency of crowdsourcing systems, more and more tasks could be distributed
to and completed by ordinary users (as workers) on the Internet via a paradigm of micro
outsourcing. The writer Howe published an article in the Wired magazine (Howe 2006),
where he deeply analyzed the impact of a rising micro outsourcing via Internet on current
business environments, and the term crowdsourcing was first introduced. The crowdsourcing
is defined by Merriam-Webster as the process of obtaining needed services, ideas, or con-
tent by soliciting contributions from a large group of people, and especially from an online
community, rather than from traditional employees or suppliers. A crowdsourcing process
involves several steps. First, a large task is partitioned into numerous small pieces of subtasks
which do not require special expertise to complete. All these small subtasks are distributed
on a platform and picked up by online workers. After finishing these subtasks, each worker
may obtain a small amount of reward. Accomplished subtasks are eventually assembled into
a solution for the original task. The amateurism and the independency of workers are funda-
mental differences between crowdsourcing and traditional employment based labor mode.
The success of crowdsourcing is due to the crowd capacity which is a series of organizational
processes through the contribution of the group’s intelligence via a public IT infrastructure
(Prpic and Shukla 2013,2014). Crowdsourcing not only has a commercial value but also has
a social significance which lies in its reconstruction of social intelligence and resources with
high efficiency to solve intelligent problems.
Crowdsourcing has already attracted a wide attention in the field of artificial intelligence.
Researchers wish that human intelligence could be involved in the computational process
and collaborate to solve the problem that cannot be conquered solely by machines, which is
so-called human computation (Von Ahn 2009). Researchers have been attempting to utilize
crowdsourcing to solve different kinds of problems (Bernstein et al. 2010;Carvalho et al.
2011;Grady and Lease 2010;Urbano et al. 2010;Corney et al. 2010;Muhammadi et al.
2015). As one of the most active branches in artificial intelligence, machine learning and
its related fields (including data mining, information retrieval, pattern recognition, computer
vision and image, etc.) are the first to realize that the development of crowdsourcing may
bring great opportunities to themselves (Lease 2011). A large amount of labeled data can be
collected quickly and cheaply via crowdsourcing, which are the cornerstone of a wide range
of supervised learning methods. On the one hand, the importance of model training is higher
than its complexity (Halevy et al. 2009). The diversity and the specificity of the data are
greatly increased. On the other hand, fast and easily generating verification and test data sets
on demand continuously optimize the iteration of learning models (Little et al. 2009), with
which a learning system will evolve to a hybrid sustained learning system (Yan et al. 2010a).
With the growing of crowdsourcing platforms, such as Mechanical Turk Amazon (MTurk)
and CrowdFlower, more and more machine learning tasks are posted on these platforms. The
tasks include the collection of ranking scores (Su et al. 2007), image and video annotation
(Nowak and Rüger 2010;Sorokin and Forsyth 2008;Vondrick et al. 2013;Xu et al. 2012)
and other online applications (Doan et al. 2011). Despite their diversity, the core of these
applications is utilizing ordinary users to assign labels to objects, and these labeled data are
used by intelligent algorithms to solve problems.
Learning from crowdsourced labeled data: a survey
Traditionally, labeling tasks are usually processed by domain experts. This way provides
accurate labels, but it is not efficient, and involves a high cost. In contrast, crowdsourc-
ing is superior to expert labeling in the cost and efficiency. However, the label qualities
in crowdsourcing are varied. This is due to significant differences among labelers in their
knowledge levels, dedication and evaluation criteria. Therefore, how to improve the quality
of the data and how to use these noisy data to build a learning model have become hot top-
ics in recent machine learning studies. In the context of crowdsourcing, traditional machine
learning techniques related to labeled data, such as classification, regression, active learning,
transfer learning, and so on, are full of new challenges. Although novel research fruits have
been coming out in recent years, research in this field is still in a very young stage, and a lot
of issues are worthy of being further studied.
In this paper, we start with the basic concepts of the qualities of labels and learning models,
review the mainstream research outcomes in the field of learning from crowdsourced labeled
data and discuss some future research directions. This paper is organized as follows. In
Sect. 2, the definitions of crowdsourced labeling, label quality and learning model quality
are presented. In Sect. 3, the researches on the ground truth inference in crowdsourcing are
reviewed. In Sect. 4, the researches on the building of learning models using crowdsourced
data including active learning and emerging transfer learning are summarized. In Sect. 5,
we summarize some open accessible real-world data sets and open source libraries and tools
for crowdsourcing studies. Sect. 6concludes the paper and discusses some future research
2 Crowdsourced labeling
In this section, we first retrospect a brief history of labeling done by the crowd. Then, we
focus on the label quality and the learning model quality.
2.1 Overview
The idea and practice of using ordinary Internet users to label data is proposed in 2004 by
Luis von Ahn. He designed a game ESP to label images (Von Ahn and Dabbish 2004). In
the ESP game, if two users provide the same labels for an image, both of them are awarded
points. Luis von Ahn then developed the famous system reCaptcha (Von Ahn et al. 2008).
reCaptcha uses two different Optical Character Recognition (OCR) systems to scan the same
document.The respective outputs of these OCRs are aligned and compared with each other.
Any word that is deciphered differently by both OCR programs will be marked as suspicious.
The images of those suspicious words are recognized by human through the way when users
sign in a system.
The success of reCaptcha shows the advantages of ordinary Internet users in solving
the problem that the machine intelligence hitherto cannot solve. Although crowdsourcing
systems had not been proposed during its development, the idea forms the basis of taking
advantage of crowdsourcing. The first time that a commercial crowdsourcing system is used
to study the label quality started in 2008, Snow et al. (2008) selected a 100-headline sample,
posted them on MTurk, and collected 1000 affect labels for each of seven emotion types,
where each example was labeled by 10 unique labelers. This work confirms that the quality
of labeling by non-expert labelers is almost the same as that of labeling by experts, if a proper
integration method is adopted. At this point, a large number of annotation tasks in machine
J. Zhang et al.
Fig. 1 A general framework for
learning from crowdsourced
labeled data
learning are completed with the help of crowdsourcing. Figure 1shows a general framework
for learning from crowdsourced labeled data.
2.2 Qualities of labels and learning models
The essence of labeling is to map the underlying features of an object to an upper level
concept using the human intelligence (Michalski et al. 2013). Although crowdsourced label-
ing provides a promising blueprint, it suffers from low qualities of non-expert labelers. At
present, since it is not allowed to modify features of objects, data quality and label quality
are synonyms in this paper.
Definition 1 (Label Quality) The accuracy that the labels of objects derived from the crowd-
sourced labeling match their true values.
In a crowdsourcing study, a widely acknowledged assumption is that the true labels come
from experts who have a perfect labeling quality known as the oracles. These true labels
are used as a gold criterion for model and algorithm evaluation. Compared with the experts,
labelers from a crowdsourcing platform have uneven expertise and dedication which result
in a low quality of the data. The label of an object, which is inconsistent with its true value,
is known as a noisy label. Reducing the noisy labels in a data set is a direct means to improve
the quality of data.
Definition 2 (Learning Model Quality) The prediction performance of a learning model
which is trained by a crowdsourced data set through a supervised learning algorithm.
Intuitively, the learning model quality is closely related to the label quality. Because of the
noise presented in labels, the quality of the learning model trained by crowdsourced data
is usually lower than that trained by expert labeled data. Thus, a direct way to improve
the learning model quality is improving the label quality first. However, the learning model
quality and the label quality are different things after all. On one hand, from the perspective
of applications, some applications end up with the true labels inferred. For example, if we
create a large-scale image database for user authentication in a login process, the requirement
is that all images are correctly labeled for the subsequent comparison with user inputs. On
the other hand, from the perspective of model learning, training a good learning model does
not necessarily utilize all examples in a training set. For example, if a support vector machine
(SVM) (Steinwart and Christmann 2008) is used to build a classifier, it should be efficient as
long as support vectors are correctly labeled (Gu et al. 2014,2015), even though the overall
Learning from crowdsourced labeled data: a survey
labeling quality is not so high. That is, in order to efficiently improve the quality of learning
models under a constraint of budget, we must improve the labeling quality on those key data
points, which can be achieved through active learning (Zhong et al. 2015).
2.3 Improving label quality
Generally, there are two ways to improve the label quality of the crowdsourced labeled data.
One is called Quality Control on Task Designing (Allahbakhsh et al. 2013). In this way,
some quality control mechanism is introduced to regulate the behaviors of labelers, which tries
to guide the labelers to provide high quality labels. For example, Dow et al. (2012) introduced
a shepherd mechanism to provide real-time quality assurance. Kittur et al. (2011), Kulkarni
et al. (2012) designed a proper workflow (including a quality supervision mechanism) to
support complex tasks. Zhang et al. (2013) proposed a strategy for the quality control during
label collection, which introduces periodical quality checkpoints to filter out low quality
labelers and labels. The common defect of these methods is that the complicated quality
control mechanism in data collection phase heavily relies on the background and historical
information. Furthermore, the complex task allocation model damages the fairness and the
efficiency, which results in a difficulty to design a reasonable reward mechanism. Currently,
most commercial crowdsourcing systems, such as MTurk and CrowdFlower, do not provide
such complicated functions. The design of the quality control mechanisms belongs to the
disciplines of human–computer interaction, collaboration, gaming or operations research. In
recent years, some researchers developed effective payment mechanisms for crowdsourcing.
Shah and Zhou (2015) proposed a simple payment mechanism to incentivize workers to
answer only the questions that they are sure of and skip the rest. This mechanism is the an
incentive-compatible payment mechanism under a mild and natural no-free-lunch require-
ment, which can minimize possible payment to spammers. Furthermore, coupling with a
(strictly proper) incentive-compatible compensation mechanism, Shah et al. (2015)intro-
duced approval voting to utilize the expertise of workers who have partial knowledge of the
true answer to solve the problem that the incentives of the workers are not aligned with those
of the requesters.
The other is called Quality Improvement after Data Collection. Current crowdsourcing
systems only provide simple trust models to filter out spammers, by which the label quality
still hardly be guaranteed. Additional procedures are used to further improve the label quality.
A common idea is repeated labeling. That is, an example is labeled by different labelers.
Therefore, each example could obtain a set of multiple noisy labels. Then, we can design
algorithms to induce an integrated label from its multiple label set. These algorithms are
called ground truth inference algorithms. We hope that the integrated label of each example
is its true label. Using repeated labeling to improve the label quality can be traced back to
1994. Smyth et al. used a maximum likelihood estimation algorithm and repeated labeling to
deal with the uncertainties in Venus image annotations (Smyth et al. 1994,1995). The ground
truth inference algorithms will be discussed in Sect. 3. The integrated labels greatly improve
the data quality, which makes the collected data to meet the needs of training models.
2.4 Challenges of learning from crowdsourcing
Both label integration and learning model training under the crowdsourcing environment
face with many challenges.
1. The expertise and dedication of non-expert labelers are different from one another. The
statistical characteristics of these variables are not very clear, which bring difficulties in
J. Zhang et al.
modeling. All these make a ground truth inference algorithm perform inconsistently on
different data sets. Moreover, there are still no effective ways to conduct model selection.
2. Crowdsourcing systems have an open nature, i.e., not all historical information of partic-
ipants can be obtained. It is impossible to make decisions based on the information other
than collected multiple noisy labels. That is, ground truth inference algorithms must be
agnostic, which do not rely on any other additional historical or supervised information.
3. The quality control in crowdsourcing is difficult, especially some systems provide some-
what privacy protection (Downs et al. 2010;Ross et al. 2010), which makes it even
impossible to improve label quality only during the data collection. That is why improv-
ing quality after collection is so important.
4. The phenomenon of biased labeling is widely spread (Wauthier and Jordan 2011;Faltings
et al. 2014). Bias is different from an individual error, which is a common tendency of
a large number of labels. It is caused not only by the differences between non-expert
labelers and experts in their expertise and their individual preference, but also by the
different scales of measures when making decisions. The detection and the modeling of
bias are very difficult, and bias has a negative effect on inference algorithms and training
5. Both spam and adversarial labelers may exist (Ipeirotis et al. 2010;Wang et al. 2014).
Spammers usually provide fixed labels to most objects, while adversarial labelers intend
to provide error labels even though they know right ones. These two kinds of labelers
are very harmful to the quality of collected data. Some studies (Wang et al. 2014;Li
et al. 2014;Tong et al. 2014;Raykar and Yu 2012) are committed to detect and clean
out the results provided by the ”malicious” labelers. But under the agnostic environment,
detections are often inefficient and conservative. Even though, a large number of non-
malicious results could be washed out.
3 Ground truth inference
Since crowdsourcing is a open labor force market place, the quality of work must be a great
challenge. Some restrictions can be affiliated to a task which ensures that labelers must com-
ply with certain characteristics such as total working time, the proportion of approvals of
their work, etc. These stipulations are setup with the hope that annotators would complete
their assigned tasks seriously. However, due to some other factors such as lack of expertise,
misunderstanding directions, preferences and so on, human error, bias, even sabotage can-
not be completely avoided. We cannot rely on a single labeler’s decision, but infer the true
labels of instances through multiple noisy labelers, which is one of effective ways to improve
the quality of labels, requiring no additional infrastructure improvement to prevalent crowd-
sourcing platforms. Lin et al. (2014) pointed out relabeling provides the highest benefit on
the domains with a large number of features (e.g., image, video).
Ground truth inference is defined as a process of estimating the true label of each example
from its multiple label set. If we only focus on the label itself, it is also called label integration.
But the meanings of the former are more wide, because a lot of inference algorithms not only
estimate the label of examples, but also estimate other parameters, such as the levels of
expertise knowledge of labelers, the difficulty of examples, and so on. In this section, we
first describe the problem of the ground truth inference. Then, we focus on expectation
maximization based ground truth inference algorithms. Finally, some other algorithms are
briefly discussed.
Learning from crowdsourced labeled data: a survey
3.1 Problem definition
For a crowdsourcing system, the sample set is defined as E={ei}I
i=1, where each example
is ei=<xi,yi>,xiis the feature vector, and yiis the true label. The labeler set is defined as
j=1. Each label is an element of the class set C={ck}K
k=1. For simplicity, labelers,
examples and classes can be denoted by their indexes, saying that example iis labeled kby
labeler j. For a binary labeling, we respectively map c1(i.e., k=1) and c2(i.e., k=2) to
the negative and the positive classes. For example i, it associates a multiple noisy label set
j=1, where element lij comes from labeler j,andlij ∈{0,c1,c2,...,ck},where0
means that the corresponding labeler does not provide any label. All multiple noisy label sets
in the data set form a matrix L={li}I
i=i. For each labeler j, there is a matrix T(j)={t(j)
ik },
where 1 iIand 1 kK. Each element t(j)
ik denotes the count number that labeler
jprovides the label kto example i. In practice, for the sake of cost and consistency, it is
not usual to allow multiple labeling by the same labeler to an example, i.e., t(j)
ik ∈{0,1}.In
addition, for a binary labeling, we define the prior probabilities of the negative and positive
classes of the data set as pand p+.
The ground truth inference is to obtain an integrated label ˆyito each sample ias its
estimated true label, and to minimize the empirical risk
Remp =1
Iˆyi= yi(1)
given the whole noisy label matrix L,whereIis an indicator function whose output will be
1 if the test condition satisfies. Otherwise, its output will be 0.
3.2 General ground truth inference algorithm: an overview
A general ground truth inference algorithm satisfies following two constraints. It at least infers
integrated labels for examples; and it does not depend on any additional information (such as
the historical labeling qualities, true labels of some examples, the features of examples, etc.)
other than observed multiple noisy labels. Majority vote (MV) is a simple but an effective
method. For a binary case, as long as more than a half labelers provide negative labels, the
integrated label will be negative, and vice versa. If the numbers of both kinds of labels are
the same, the integrated label will be randomly determined. Both Sheng et al. (2008)and
Ipeirotis et al. (2014) carefully studied the algorithm MV and proposed a simple probabilistic
model to describe the label quality of a single example sample. This model assumes that each
labeler has the same labeling quality p. If each example obtains 2N+1 labels, the probability
of the integrated label to be true obeys a binomial distribution
Since labelers obey the same probabilistic model, when the number of noisy labels for each
example increases, the accuracies of integrated labels are quickly improved. Thus, this model
provides an upper boundary of the performance of inference algorithms. For a multi-class
labeling case, this algorithm is call plurality vote (Parhami 1994). In this paper, we do not
distinguish the differences between MV and plurality vote, and use MV for simplicity. Jung
and Lease (2011) proposed a method to improve the accuracy of MV using z-score and
weights for examples.
J. Zhang et al.
Besides MV, quite a few novel general ground truth inference algorithms have been pro-
posed in recent years. Table 1summarizes these algorithms and makes comparisons from
different aspects. These algorithms can be classified into two categories according to their
mathematical methodologies: machine learning-based and linear algebra-based methods.
Machine learning based methods are the main stream of current researches, where inference
models are usually based on the probabilistic graphical models (Koller and Friedman 2009).
One important mainstream general method that conducts inference in probabilistic graphi-
cal models is expectation-maximization (EM) algorithm (Watanabe and Yamaguchi 2003)
which is at present widely used in the estimation of latent variables (Li et al. 2015;Liu et al.
2015). Since the majority of ground truth inference algorithms are based on EM, we focus
on this kind of algorithms in the next section. From the label type viewpoint, most inference
algorithms can be applied to binary labeling, and only a few are suitable for multi-class label-
ing. There are two studies related to ordinal labels. As Fig. 1shows, in most cases inference
and model learning are loosely coupled. However, there exist a small amount of methods,
where inference and model learning are closely coupled. That is, during the process of label
inference, the learning model is simultaneously trained. Such methods usually need to utilize
the features of the examples. If the features cannot be obtained, the algorithms degrade into
pure inference algorithm. For all general ground truth inference algorithms, although they
model different aspects of a crowdsourcing system, a learning model can be trained after
inference as long as the features of examples can be acquired.
Crowdsourcing is based on two basic common assumptions: labelers have different reli-
abilities and independently make decisions. Some inference algorithms only follow these
assumptions, such as MV, DS (Dawid and Skene 1979), and ZenCrowd (Demartini et al.
2012). However, other ones, such as RY (Raykar et al. 2010), IEThresh (Donmez et al.
2010), PLAT (Zhang et al. 2015c), and LC-ME (Tian and Zhu 2015), are designed based
on some other additional assumptions. Assumptions of an inference algorithm determine on
which conditions it reaches its good performance. For example, RY (Raykar et al. 2010)
assumes that labelers have biases toward negative and positive examples. If real-world sit-
uations follow this assumption, RY performs well. Otherwise, it may perform not so well.
We list the special assumptions of reviewed ground truth inference inference algorithms in
Tabl e 2.
3.3 Inference based on EM
Early in 1979, Dawid and Skene proposed a ground truth inference algorithm DS based on
maximum likelihood estimation (Dawid and Skene 1979). In addition to infer the integrated
label for each example, DS also estimate a confusion matrix for each labeler. The element
kl in the confusion matrix of labeler jdenotes the probability that the labeler provides
the label lto the example with the true label k. In E-step, DS estimates the probability that
example ibelongs to the class kas
kl tj
il P(ck)
ql tj
il P(cq)
In M-step, DS updates the confusion matrix of each labeler and the prior probability of each
class as follows.
Learning from crowdsourced labeled data: a survey
Tab l e 1 Comparisons among general ground truth inference algorithms
Algorithm Main function Label type Methodology Reliability of
Difficulty of
Other comments
MV Sheng et al.
Inference Binary Statistics Majority voting
DS David and
Skene (1979)
Inference Multiclass EM Model confusion
matrices of
GLAD Whitehill
et al. (2009)
Inference Binary EM √√
IEThresh Donmez
et al. (2009)
Inference Binary Statistics √√Interval estimation
Welinder et al.
Inference Binary EM √√Model
competence and
bias of labelers
Welinder and
Perona (2010)
Inference Multiclass/ordinal EM √√Optimize the
number of labels
RY Raykar et al.
Inference +
Binary EM Model sensitivity
& Specificity of
Weighted voting
Jung and Lease
Inference Multiclass Statistics Filter unreliable
KOS Karger et al.
Inference Binary Algebra Minimum the
cost; belief
J. Zhang et al.
Tab l e 2 Special assumptions of reviewed ground truth inference algorithms
Algorithm Assumptions
GLAD Whitehill et al. (2009) Different levels of expertise of labelers
Different levels of difficulty of examples
IEThresh Donmez et al. (2009) A learner should acquire labeler and label knowledge through
repeated trials, balancing the exploration versus exploitation
tradeoff by first favoring the former and moving gradually to
increase exploitation
Welinder et al. (2010) Each image has different characteristics that are represented in an
abstract Euclidean space
Welinder and Perona (2010) For a label provided by an expert annotator, we can probably rely
on it
For labels provided by unreliable annotators, we should probably
ask for more labels until we find an expert or until we have
enough labels from non-experts to let the majority decide the label
RY Raykar et al. (2009,2010) Labelers have biases towards negative and/or positive examples
KOS Karger et al. (2011,2014) Using a simple model to capture the presence of spammers, which
is called the spammer-hammer model
Yan et al. (2010c,2011) The probability of a labeler providing true labels obeys a Bernoulli
or Gaussian distributions
Ghosh et al. (2011) If a labeler has the probability of providing correct answers greater
than 1/2, the labeler can be identified as a trustworthy labeler
Liu et al. (2012) Different levels of ability of labelers
The same level of difficulty of examples
Kajino and Kashima (2012) Introducing a personal classifier for each of workers, and
estimating the base classifier by relating it to the personal models
Zhou et al. (2012) When many items are simultaneously labeled, the performance of a
worker is consistent across different items
PLAT Zhang et al. (2015a) Labelers have different correction rates on negative and positive
Kurve et al. (2015) A stochastic model for answer generation that plausibly captures
interplay between worker skills, intentions, and task difficulties
LC-ME Tian and Zhu (2015) Each item belongs to one latent class, and labelers have a consistent
view on items of the same class but inconsistent views on items
of different classes
Several different latent classes consist in one label category
GTIC Zhang et al. (2015a,b,c,d) Labelers’ biases from one class towards another show some
consistency in a multi-class categorization
kl =
il K
il (4)
Although DS has been widely used in (Snow et al. 2008;Smyth et al. 1994,1995;Ipeirotis
et al. 2010;Rodrigues et al. 2013;Eagle 2009), it has a defect: if the number of classes
is large and the number of labels is relatively small, the estimated confusion matrix is very
Learning from crowdsourced labeled data: a survey
sparse, which results in inaccurate estimation. Recently, DS has been considered to be a good
theoretical model to analyze the error rate bounds of label aggregation. Li and Yu (2014)
derived error rate bounds of a general type of aggregation rules with any finite number of
workers and items under the DS model, which can be used for designing optimal weighted
majority voting. Khetan and Oh (2016) also analyzed the reliability of crowdsourcing under
the generalized DS model.
Focusing on binary labeling, Raykar et al. (2009,2010) proposed a Bayesian estimation
based algorithm RY, which reduces the number of parameters, compared with DS. It only
concerns two parameters sensitivity and specificity. In their model, sensitivity parameter αj
denotes the labeler’s bias towards the positive and the specificity parameter βjdenotes the
bias towards the negative. The two parameters and the prior probability of the positive class
obey a Beta distribution (Gelman et al. 2014).
Parameters a+
j) are the numbers of the positive and the negative labels
respectively, provided by labeler jto those examples currently estimated as positive (neg-
ative). Parameters n+and nare the total numbers of the positive and the negative labels
respectively, provided by all labelers. RY estimates the probability that example ibelongs to
the positive in its E-step as P(yi=+|xi,L, p+). In M-step, RY updates the parame-
ters (sensitivity, specificity) and the prior probability of the positive class. RY shows some
advantages over DS for a binary labeling. However, for a multi-class labeling, no empirical
investigation has been reported.
Both algorithms above do not take the difficulties of labeling examples into account. Some
studies (Welinder et al. 2010;Brew et al. 2010) have shown that considering the difficulties
of examples is useful. The algorithm GLAD (Whitehill et al. 2009) models both the levels of
users’ expertise and the difficulties of examples. GLAD uses the parameter 1i[0,+∞)
to model the difficulty of example iand the parameter αj(−∞,+∞)to model the level
of the expertise of labeler j. The larger the 1i, the harder an example can be labeled; and
the lager the αj, the more professional a labeler would be. αj<0 suggests that labeler jis
adversarial. GLAD adopts a logistic regression model as follows.
P(lij =yi|αj
In E-step, based on the two parameters (α,β) and all observed labels, GLAD calculates the
posterior probabilities of being positive and being negative for all examples as follows.
P(yi|L) P(yi)
In M-step, GLAD uses a gradient descent algorithm to maximize a standard auxiliary function
Qand updates the two parameters (α,β), where function Qis defined as follows.
E[ln P(yi)]+
E[ln P(lij|yij
Welinder et al. (2010) proposed a more complex multidimensional model. In their model,
parameter zi∈{0,1}represents a two-value judgment to an example. When labeler jjudges
J. Zhang et al.
example i, the decision is not directly made by evaluating xi, but is made based on the
evaluation of rij =xi+nij,wherenij is a labeler-specific and instance-specific noise.
These noises vary with different labelers, each of which is parameterized as σj. Each labeler
is parameterized as a vector ˆ
wj, which encodes the expertise of a labeler to a high dimensional
space. The scalar project of <rij,ˆ
wj>is compared with a threshold ˆτj. If the former is larger,
then lij =1. Otherwise, lij =0. The inference of the method is based on a complicated
model as follows.
Demartini et al. (2012) found that RY, GLAD, and other algorithms (Welinder et al. 2010)
attempted to model the system from different aspects. However, due to the sparsity of the
sample, the accuracy of the inference faces with some risks. The simplified model may
perform better than a complex one under the sparsity of data. Thus, they proposed algorithm
ZenCrowd, which uses only a two-element {good, bad} parameter to model the reliability
of a labeler. The advantage of using one parameter is that it avoids the problem of the large
estimation deviation of the variables when applying inference on the data set with sparsity.
ZenCrowd is slightly more complex than MV, but simpler than other EM based algorithms. If
no prior information is provided, it starts with P(uj=reliable)=0.5 based on maximum
entropy principle. In E-step, ZenCrowd calculates the probability of each example belonging
to a particular class as follows.
In M-step, since the label of each example is estimated as the class with the maximum
probability, ZenCrowd uses these estimated labels to update the reliability of each labeler.
i=1I(lij yi)
Although the above algorithms respectively model the ability, the bias of labelers, and the
difficulties of examples, they cannot tell whether a labeler is a spammer or adversarial. For
example, some labelers do have good abilities, but for some reason, they provide opposite
labels. Kurve et al. (2015) added the intention of labelers into the model for inference,
where labelers are classified into two categories: honesty and adversarial. The parameters
of their model are as follows: ˜
di(−∞,+∞)represents the difficulty of example i;
labeler’s intention is denoted by vj∈[0,1], where 0 means adversarial and 1 means honesty;
dj(−∞,+∞)represents the expertise of labeler j; and the additional αjdenotes the
tendency of labeler jproviding correct labels given the difference dj˜
di. The parameter
set of their model is denoted by Λ={{(v j,djj)j},˜
dii}. Supposed the iteration count
of the algorithm is t, it calculates the labels of examples and the parameters in E-step as
Learning from crowdsourced labeled data: a survey
In M-step, it optimizes the expectation of the log likelihood of the whole data set with respect
to the parameters. The optimal parameters will be used in the next E-step.
Λt+1=arg max
E[log Lc(Λ)|E](16)
The minimax entropy principle can be used for estimating the true labels from the judge-
ments of a crowd of nonexperts. Zhou et al. (2012) combines the ideas of MV and confusion
matrix. Their work is based on an assumption that labels are generated by a probability dis-
tribution over workers, items, and labels. By maximizing the entropy of this distribution, this
method naturally infers item confusability and worker expertise. By minimizing this entropy,
the method infers the integrated labels. Let pijd be the probability of labeler jproviding a
correct label to example iwhose true label is d. According to MV, the true label of exam-
ple ishould most commonly appear in its multiple noisy label set. Thus, we have the first
pijd =
I(lij =d), (i,d). (17)
According to confusion matrix, the same labeler should have a consistent performance on
different examples belonging to the same class. Thus, we have the second constraint
I(lij =d), (j,d)
Finally, by applying the minimax entropy principle, we use the set of class Cto minimize the
entropy under above two constraints (i.e., Eqs. 19,20), and use the set of labeler probabilities
Pto maximum the entropy.
pijd log pijd (19)
Tian and Zhu (2015) proposed a nonparametric model LC-ME which extends the min-
imax entropy estimator to learn latent structures. The LC-ME estimator is based on two
assumptions: each example only belongs to one latent class and the behaviors of labelers are
different when the examples that they label belong to different latent classes. Experimental
results on real-world data sets have shown that LC-ME is superior to the original minimax
entropy estimator.
Although the inference algorithms based on expectation maximization are widely used
in the processing of crowdsourced labeled data, they are not robust on different kinds of
data sets because of the inherent defects of EM (Zhang et al. 2014). First, the likelihood
function of EM is non convex, which makes the EM algorithm cannot converge to the global
optimal. Secondly, there is no guidance to set the initial values of the parameters in EM, and
different settings result in different results on the same data set. Finally, the iteration count
of EM when it converges depends on the initial settings and the data set. There is no way
to guarantee that it can converge fast and efficiently. Some work attempts to overcome the
defects of EM. Zhang et al. (2014) utilized a spectral method (Bernardi and Maday 1997)to
estimate the initial values in the confusion matrix of each labeler before the algorithm DS
starts. In their method, all the labelers are divided into three disjoint groups. The average
confusion matrix of the three groups is estimated. Then, the initial state of the confusion
J. Zhang et al.
matrix of each labeler is estimated using the average confusion matrix. Empirical study has
shown that the performance of DS is improved by the spectral base initialization compared
with randomly settings.
3.4 Inference based on linear algebra and statistics
Besides EM being widely used for inference, some other methods are based on linear algebra
or simple statistics.
Karger et al. (2011,2014) proposed a label integration algorithm based on the reliabilities
of labelers. The algorithm uses a belief propagation-like method to achieve the goal of
inference. The following algorithm KOS shows its main steps. One notable feature of this
method is that when the noisy label sets Lis a (l,r)-regular bipartite graph with l=r,itis
equivalent to a singular value decomposition (SVD) of a low rank matrix. Ghosh et al. (2011)
proposed a similar SVD-based method, which aims to infer the ratings of user generated
Algorithm 1 KOS
Input: E,L,kmax
Output: E, where example iis assigned ˆyi
1: (i,j)Einitialize p(0)
jiwith random zij N(1,1)
2: for k=1,...,kmax do
3: (i,j)E,s(k)
4: (i,j)E,p(k)
5: end for
6: i,sijilij p(kmax 1)
7: i,ˆyisign(si)
8: return E
The algorithm KOS is not a standard belief propagation model, though it has good
guarantees on (locally tree-like) random assignment graphs, but does not have an obvi-
ous interpretation as a standard inference method on a generative probabilistic model (Liu
et al. 2012), which makes it difficult to either extend KOS to more complicated models or
adapt it to improve its performance on real-world data sets. Liu et al. (2012) proposed a
standard belief-propagation-based method based on variational inference (Wainwright and
Jordan 2008) on a generative probabilistic model. Both KOS and MV are special cases under
this framework. For example, if the reliabilities of labelers obey Haldane prior (Zellner 1996),
the method is equivalent to KOS.
SVD based methods require that the label matrix is full, i.e., all examples are labeled
by every labeler. However, in reality, this prerequisite cannot always been guaranteed. The
method proposed by Dalvi et al. (2013) also take advantage of SVD, but looses the above
prerequisite. The model introduce an additional matrix G∈{0,1}I×Jto represent whether
labeler jprovides a label to example i. The estimated vector of the reliabilities of labelers is
denoted by ˆ
w∈[1,1]Jand the estimated vector of the quality of examples is denoted by
q∈[1,1]I. The algorithm take the advantage of the (scaled) top eigenvector of a matrix
defined as v1(M)=arg minx
2,x(1)0. The steps of inference are shown in
the algorithm EigenRatio. The operator in the algorithm is defined as follows.
Learning from crowdsourced labeled data: a survey
(MN)ij =Mij/Nij,if Nij = 0
0,otherwise (20)
Besides the ground truth inference based on linear algebra, there exist some other methods
based on statistics. Donmez et al. (2009) proposed an accuracy model IEThresh based on
interval estimation (IE). In statistics, given rjobservations of variable ujfrom distribution
d, IE method estimates an interval that the next observation belongs to, with the probability
1α. IEThresh selects a labeler with the highest accuracy interval upper bound. A higher
upper bound indicates a higher expected accuracy (when the interval length is short) or a
higher uncertainty (when the interval length is long). The interval upper bound is estimated
as follows.
Algorithm 2 EigenRatio
Input: L∈{1,0,1}I×J,G∈{0,1}I×J
Output: ˆ
1: ˆ
2: ˜wj=sgn(˜wj)max(wj|,1)
3: ˆqi=sgn(jLij ˜wj)
4: return ˆ
where μ(uj)and σ(uj)are the mean and the standard deviation of correct labels provided by
labeler jrespectively. orj1
α/2is the value of t-student distribution when the degree of freedom
is rj1 and the level of confidence is α/2. When the observed data is sufficient, even though
high quality labels are very rare, IEThresh can lead to very good results.
The labeling bias is always one of research issues in learning from crowds. Many algo-
rithms (Welinder et al. 2010;Raykar et al. 2009,2010;Kurve et al. 2015) model the labeling
bias. On the real-world data sets, these EM-based algorithms probably quickly converge
to a local optimal, leading a poor result. Zhang et al. (2015c) analyzed the biased labeling
phenomenon in real-world data sets and found that most labelers usually have the consis-
tent tendency when biased labeling occurs. At this point, the class distribution of the labels
significantly skew. Based on simple statistics and heuristic search strategies. Zhang et al.
(2015c) proposed an algorithm PLAT, which counts the number of positive labels for each
example, and then automatically searches a threshold to classify examples into two cate-
gories. PLAT has obvious effectiveness on solving the biased labeling problem together with
the imbalanced class problem (Prati et al. 2015), and its running speed is higher than that
of the EM based algorithms. Besides, it can be adaptively applied to the unbiased data sets.
Bias in multi-class labeling is hard to model, which results in a poor performance for most
of inference algorithms. Zhang et al. (2016) proposed a novel algorithm GTIC based on
Bayesian statistics for multi-class labeling. For a Klabeling case, GTIC utilizes the repeated
label sets of examples to generate features. Then, it uses a K-Means algorithm to cluster all
examples into Kdifferent groups, each of which is mapped to a specific class. GTIC captures
the tendency of labeling biases with respect to groups of examples and shows a significant
improvement on multi-class inference.
3.5 Other ground truth inference algorithms
Besides general ground truth inference algorithms, there are other types of algorithms that are
used to describe different characteristics of crowdsourcing system or solve different problems.
J. Zhang et al.
Donmez et al. (2010) proposed a time varying algorithm SFilter for user accuracy mod-
eling. The objective of SFilter is to filter out low-quality labelers in active systems. This
algorithm is based on Sequential Bayesian Estimation, and assumes that the maximum rate
of changes are small and known. Let pt
jdenote the accuracy of labeler jin time t,andlt
denote the label provided by the labeler. The posterior probability of the labeling accuracy
in time tis denoted by P(pt
j). SFilter uses the following Hidden Markov model
for modeling the accuracy changes.
The model shows that the accuracy of each labeler is only determined by its accuracy in the
last time. SFilter assumes that the accuracy is in a range (0.5,1], and it calculates a transition
probability from time t1totusing a truncated Gaussian distribution as follows.
where φis Gaussian probability density function and is its cumulative distribution function.
Supposed that lt
ij is the label provided by labeler jto example iand lt
iJ(t)are labels provided
by other labelers. We have
where P(yi|lt
iJ(t))is calculated by the probability of the integrated label inferred from lt
One of the major drawbacks of SFilter is that it is time consuming. As long as one label
updates, the model needs to be re-calculated. To conquer this defect, the authors provided an
incremental version of the algorithm. Experimental results show that when the accuracy of
labels change in a certain range, SFilter can capture these changes.
Tang and Lease (2011) proposed a semi-supervised ground truth inference algorithm
based on DS. In addition to unlabeled data set DU, the algorithm needs another data set DL
involved in inference, in which the true labels of examples are known. The difference from
DS is that a term of likelihood derived from the labeled examples is added at the end of the
original likelihood function of DS as follows.
km =
km )t(j)
km t(j)
im (26)
Experimental results show that only a small amount of supervised examples can greatly
improve the accuracy of inference. Adding the expert labeled examples violates the agnostic
feature of the general inference algorithm. Similar work includes the semi-supervised model
proposed by Yan et al. (2010b), where the integrated labels and the expertise of labelers can
be simultaneously inferred. Different from Tang and Lease (2011), the model relaxes the
prerequisite that all examples must be labeled. That is, there are three kinds of examples:
those labeled by experts, by crowdsourced labelers and unlabeled. The applicability of this
semi-supervised model is obviously higher.
Learning from crowdsourced labeled data: a survey
Another algorithm ELICE proposed by Khattak and Salleb-Aouissi (2011) also focuses
on the optimization of the ground truth inference using expert labeled data, where expert
labels are injected into the crowdsourced data set. ELICE introduces two parameters αj
[−1,1]and βi∈[0,1]to represent the reliability of labeler jand the difficulty of example
irespectively. By injecting examples labeled by experts and analyzing the noisy labels on
these examples, ELICE calculates the parameters αjand βi, and then infers the integrated
label as follows.
The symbol ILirepresents the positive and negative categories. Although ELICE shows its
effectiveness, some issues need to be addressed. For example, when and how many the expert
labeled examples should be injected.
All of the ground truth algorithms mentioned above can be used to deal with either binary
or multi-class labeling problems. Even if a few algorithms studied the inference for ordinal
label (Welinder and Perona 2010), they still treat it as a special case of multi-class. Zhou
et al. (2014) proposed an inference model specially for ordinal labels, which is based on the
former minimax entropy principle (Zhou et al. 2012). The model extends the original item
confusion vector to a structural item confusion matrix. By introducing structural sequential
relationships, the model reduces the number of parameters during the inference, which is
beneficial to distinguish adjacent classes.
4 Learning model
Compared with the ground truth inference, studies of building the learning model are still in
a young stage. First, the label quality is the basis of a learning model quality. After achieving
a relative good quality of integrated labels, the model training can become an independent
process. That is why so many researches focus on improving the performance of ground
truth inference. Second, the study of the learning model is only one aspect of the studies
of crowdsourced labeling, but almost all of follow-up studies, such as information retrieval,
must concern the improvement of the inference. Finally, the performance of a learning model
is closely related to the features of examples and the selection of classification algorithms, so
it is more difficult to propose general methods that work well under most conditions. In this
section, we summarize the methods of building the learning model from the crowdsourced
labeled data in recent years.
4.1 Ordinary supervised learning
It is straightforward to build a learning model on a data set with integrated labels. For example,
RY introduces the logistic regression model
to build a classifier which is used for predicting unlabeled examples. This method provides
a general model to conduct binary classification.
A simpler method is proposed to build a learning model after inference with MV. Sheng
(2011) proposed an enhanced algorithm MVBeta, which considers the distribution of two
classes of noisy labels on each examples. The method provides a weight to each example
after conducting MV, which is defined as follows.
J. Zhang et al.
W=1min{I0.5(α, β), 1I0.5(α, β)}
where Lpand Lnare the numbers of positive and negative labels respectively in the multiple
noisy label set of an example. The function Iis the cumulative distribution function of Beta
distribution, which is defined as
Ix(α, β) =
These weights can be used by different classification algorithms in different forms. For
example, a cost sensitive tree classifier (Ting 2002) can directly utilize these weights. Sim-
ilarly, Naive Bayes classifier also can handle this kind of label with a numeric uncertainty.
Experimental results show that assigning weights to examples can significantly improve the
accuracy of the classifier in the case of high noise.
In addition to building learning models after inference, there exist some methods that
directly build learning models without processing inference first. Sheng (2011) studied a
more general method than MVBeta to build the learning model without inference, which is
named PairwiseBeta. Supposed an example has a multiple noisy label set {+++−−−−},
PairwiseBeta does not infer the integrated label for the example, but duplicates this example
into two replica {(+,WP)}and {(,WN)}. Each replica has a corresponding weight. The
weights of these two replica are defined as follows.
After example duplication, all examples with their weights are processed by a cost-sensitive
classifier to train a learning model. Since this method preserves the information of multiple
noisy label sets at a maximum extent without any bias, it is superior to MV and MVBeta in
most cases.
As mentioned in Sect. 3, the mainstream methods of ground truth inference are based
on the EM algorithm. Due to the inherent defects of EM, inference easily converges into a
local optimal, which directly leads to a poor learning model. Kajino and Kashima (2012)
proposed a method, which directly builds learning models without the prepositive inference.
The objective of their method is to construct a convex function, and then to build a logistic
regression model by convex optimization. The method first introduces a base classifier as
Then, each labeler is treated as an independent classifier with a parameter wj.
The parameter of each independent classifier can be viewed as the parameter of the base
classifier plus a deviation vector.
If each labeler is treated as a classifier to handle a batch of tasks, the model is similar with
the multi-task learning proposed by Evgeniou and Pontil (2004). Given W={wj}J
j=1, its
target convex functions is
Learning from crowdsourced labeled data: a survey
where δ(s,t)=slog t+(1s)log(1t),Ijrepresents all examples labeled by labeler
j, and both λand ηare positive constants. The model finally optimizes the target function
F(w0,W)with respect to the parameters w0and W. This method theoretically obtains a
global optimal solution, and their experimental results shows that it is better than RY.
Learning with crowdsourced labeled data is implicitly related to learning with noisy labels
(Natarajan et al. 2013). In traditional supervised learning, when training data are polluted
by label noises, obvious solutions are to cleanse the training data or to design label noise-
tolerant models. Designing label noise-tolerant models, such as (Rätsch et al. 2000;Freund
2001;Sukhbaatar et al. 2014), are not easy. Experimental results in the literature show that
the performance of classifiers trained with label noise-tolerant algorithms is still affected by
label noises. They seem to be adequate only for simple cases that can be safely managed
by overfitting avoidance (Frénay and Verleysen 2014). Label noise cleansing methods, such
as model prediction-based filtering (Brodley and Friedl 1999) and label noisy correction
(Nicholson et al. 2015), are easy to understand and implement. However, some evidences
have shown that precisely distinguishing mislabeled instances from correct ones may be
rather difficult (Weiss and Hirsh 1998). Frénay and Verleysen (2014) pointed out that in most
cases simply removing mislabeled instances is more efficient than correcting them. However,
in the most cases of crowdsourcing tasks, due to the limitation of budgets, removing a large
number of potential examples with noisy labels is not acceptable. Some information obtained
in an inference stage may help us precisely identify potential noisy labels and make a better
correction (Zhang et al. 2015b). Noise correction in crowdsourcing is worthy of being studied
in the future.
4.2 Active learning
In many real-world applications, it is necessary to construct a learning model. When there
is no enough data available, we need to actively acquire extra data from the oracle. This is
active learning (Settles 2010). When the extra data acquisition tasks are outsourced to the
crowd, it is called crowdsourcing. From the perspective of model learning, not all instances
necessarily need to be labeled and not all labeled instances necessarily have the same number
of repeated labels. Active learning allows to dynamically require labels, which is suitable for
the open and dynamic environment of crowdsourcing. One of the most important features
of active learning is to reduce the total cost of acquiring labels in the premise of keeping
the performance of learning model (Settles and Craven 2008). The core of traditional active
learning is to design instance selection strategies (Fu et al. 2013). In the crowdsourcing
environment, in addition to the instance selection, labeler selection is optionally included.
Labeler selection strategies aim at selecting the next labeler to provide labels who is the most
beneficial to the improvement of the current model quality. In an active learning paradigm,
instance selection is compulsory. Figure 2shows a general active learning framework for
learning with crowdsourced labeled data. In the framework, examples are both labeled and
unlabeled. Selected examples are labeled by multiple selected labelers. After inferring the
integrated labels of the labeled examples, a learning model is trained and evaluated. If the
learning model cannot be further improved or satisfies a preset condition, the iteration of
active learning ceases.
J. Zhang et al.
Fig. 2 A general framework for
active learning from
crowdsourced labeled data
S1, S2,
G1, G2,
Sheng et al. (2008) first investigated the instance selection strategies in crowdsourcing.
(1) The simplest strategy in crowdsourcing is to select the example whose multiple noisy
label set contains a minimum number of noisy labels. This ensures that each example has
the equal opportunity to get labels. Obviously, this simple strategy wastes budget and has
poor performance. (2) Instance selection strategies can be designed based on heterogeneity
of labels. One of these strategies can use information entropy to measure the heterogeneity
of examples. Supposed that in the multiple noisy label set of example xithe proportion of the
majority class is pi. This strategy can select example lthat satisfies the following condition.
l=arg max
That is, this strategy selects those examples, in whose multiple noisy label sets the proportions
of the major classes are close to 0.5. However this strategy could cause that a small portion
of examples are always selected again and again, and obtain enormous labels, while other
examples are probably neglected, because it does not concern the numbers of labels obtained
for each example. (3) Instance selection strategiescan be designed based on label uncertainty.
They proposed such a strategy, which only considers the class distribution of labels in the
multiple noisy label set of each example. It selects the example with the maximum label
uncertainty to query extra labels. The label uncertainty measure is defined as follows.
UL=min{I0.5(Lp+1,Ln+1), 1I0.5(Lp+1,Ln+1)}(37)
where Lpand Lnare defined as the same as Eq. (29). (4) Instance selection strategies
can be designed based on model uncertainty. They proposed such a strategy, which only
considers uncertainties of examples to the current learning model, but totally ignores the class
distribution of labels. This strategy adopts ensemble learning to build mindependent weak
classifiers, which work together to determine the probabilities of the classes of examples.
The probability of an example being classified as positive is calculated as follows.
where P(+|xi,Hj)is the probability of example xibeing classified as positive by model Hj.
The learning model can be trained using Random Forest (Breiman 2001). The uncertainty
measure of an example is defined as the distance between 0.5 and the estimated probability
of being positive or negative.
Learning from crowdsourced labeled data: a survey
(5) Instance selection strategies can be designed based on hybrid uncertainty. They proposed
such a strategy, which combines two strategies above and the uncertainty measure of each
example is defined as follows.
ULM =UL·UM(40)
Experimental results show that their hybrid strategy, which combines two mutual comple-
mentary uncertainties, has the best performance in most cases. Zhang et al. (2015d) extended
these strategies by taking the bias information into account, which makes them to handle
biased labeling issues.
Long et al. (2013) proposed a novel instance selection method based on entropy, where
the uncertainty measure of an unlabeled example xuis defined as follows.
pyu|xu,DLlog pyu|xu,DL(41)
where p(yu|xu,DL)can be solved by an expectation propagation method (Williams and
Barber 1998).
Currently, the studies of instance selection in crowdsourcing learning are not sufficient.
Novel instance selection strategies are expected. When we design a new instance selection
strategy, we must pay attention to the following points: (1) the status of the multiple noisy
label set of each example must be taken into account; (2) we should estimate the performance
changes of the model (better maximize the model performance) when the selected instances
obtain additional labels; (3) we should avoid local optimals.
Yan et al. (2011) proposed an active learning framework based on the logistic regression
model proposed in their previous study (Yan et al. 2010c). In this framework, labeler selection
is involvedas well as instance selection. Each labeler is viewed as logistic regression classifier.
The logistic regression function of labeler jis defined as follows.
The overall final logistic regression classifier is defined as
The instance selection procedure chooses the example with the probability P(yi=1|xi)
mostly close to 0.5. After the example is selected, a labeler selection procedure chooses
labeler jwith the minimum uncertainty to label the selected example.
j∗=arg min
jσj(xi), j(44)
Due to selecting confident labelers to provide labels, the performance of this method is better
than that of the methods only involving instance selection.
One defect of the above labeler selection method is that those who are reliable are repeat-
edly selected, and ordinary labelers are lack of opportunities. In order to select labelers in
a wider range and also improve the ability of ordinary labelers, Fang et al. (2012) proposed
a self-taught active learning method. The self-taught procedure in the method is essentially
using the labels provided by weak labelers to extend the multiple noisy label sets of examples.
The main steps are: (1) select an example xto query by some instance selection strategy; (2)
select a reliable labeler jby some labeler selection strategy; (3) labeler jlabels example
xwith label lxj; (4) select the most unreliable labeler w;and(5)GivenIwis the label set
provided by labeler w,letIwIw∪{x,lxj}. The instance and labeler selection strategies
J. Zhang et al.
used are the same as those proposed by Yan et al. (2011). By adding new reliable labels into
the label sets of weak labelers, reliable knowledge is learned by weak labelers. Although
it is a very simple method, the experimental results show the obvious improvement of the
model quality during active learning. Rodrigues et al. (2014) proposed an active learning
framework based on Gaussian process, in which different levels of expertise of labelers are
model, instances and labelers are also selected according to their uncertainty and reliability.
For a classification problem, labelers must make choices when providing answers. How-
ever, the confidences of labelers are different from one another. Zhong et al. (2015) introduce
the labeling confidence into active learning. For a binary labeling, the class set of labels is
{−1,0,1}, where 0 indicates that a labeler is unsure about label to provide. Having com-
pleted the ith query, a classifier fi(x)is built. Let function gj(x)be the reliability of labeler
j.gj(x)>0 means that the labeler provides a correct label to example x. Define function
jJδ(gj(x)),whereδis a symbolic function. The instance and labeler selection
strategies are respectively presented by Eqs. (45)and(46).
xi+1=arg max
i)·(x)) (45)
ji+1=arg max gt(xi+1)
In order to embody the strategies, SVM with Radial Basis Function kernel is applied to build
a classifier. Experimental results on real-world data sets show the performance of this method
is better than that of the baseline and PMActive proposed by Wu et al. (2013). It is reasonable
that the confidence measures of the labelers towards their provided labels are introduced in
the learning model. However, how to extend the current binary value confidence measure to
a real value measure is worthy of further studies.
4.3 Other learning model
Learning from crowdsourced labeled data is a new research field. Many research issues in
traditional machine learning are facing new challenges. For example, researchers have been
aware of the potential value of recent widely studied transfer learning (Pan and Yang 2010;
Oyen and Lane 2015) to crowdsourcing. Mo et al. (2013) first introduced the techniques
of transfer learning in the area of crowdsourcing and proposed the concept of cross-task
crowdsourcing which shares the knowledge across different domains and solves the problem
of the knowledge sparsity of a particular domain. They gave us a vivid example. After a
batch of labelers complete the tasks of labeling razor images, it is easy to infer the gender
information of these labelers. The inferred gender information transferred from the source
domain will be helpful when we select high quality labelers in active learning to label fra-
grance brand images. This eventually results in a better performance of learning model. The
proposed method is based on a probabilistic graphical model, and its inference procedure
utilizes Markov Chain Monte Carlo (MCMC) and gradient descent algorithms.
The transfer learning model can be combined with active learning to provide more plentiful
information for instance and labeler selection. Fang et al. (2013) proposed a framework that
combines knowledge transfer and active learning. As Fig. 3shows, in this framework the
expertise levels of labelers are modeled from historical labeling information in a source
domain, and then used in a target domain to conduct instance and labeler selection. The
proposed method jointly considers the probability distributions of different types of labels in
both source and target domains. Experimental results on real-world data sets show that the
Learning from crowdsourced labeled data: a survey
Fig. 3 A framework for transfer
learning from crowdsourced
labeled data (Fang et al. 2013)
proposed method can significantly improve the performance of active learning if the target
domain is similar enough to the source domain.
5 Data sets and tools
Although crowdsourced labeled data is a kind of user generated content, they are really
significantly different from social tags from Internet. Crowdsourced data come from the
realistic demands of requesters, and its labeling process is accompanied with the expectation
of real economic rewards. After labeling, requesters optionally approve answers from labelers
to confirm corresponding payments. Because of involving economic interests, the quality of
crowdsourced data appears to be higher than ordinary social tags. In fact, it is not the case.
On one hand, immature quality control mechanisms cannot guarantee that user will not
cheat requesters. On the other hand, once users accept tasks, regardless of the level of their
expertise in the application domain, they will provide answers for the sake of rewards. The
cost of crowdsourced data collection is higher than that of social tags, but much lower than
that of collecting from experts. Currently, crowdsourcing still is a novel research domain
less than ten years. Most of the crowdsourced data were collected by researchers themselves,
and scattered in their research papers and related academic conferences. To facilitate the
future researches in this field, this section lists some information about the open accessible
real-world crowdsourced labeled data set and several open source tools.
5.1 Real-world data sets
The earliest contribution of publishing real-world crowdsourced labeled data sets can be
traced back to 2008. In order to study whether the crowdsourced label can be really used
for natural language processing, Snow et al. (2008) chose data sets containing fine natural
language processing tasks, posted them on MTurk and requested users from Internet to
label them. The collected data sets are open to the public for research purpose. After that,
many researchers chose to publish their data sets collected from crowdsourcing systems in
their websites along with their research papers (Ipeirotis et al. 2010;Whitehill et al. 2009,
2010;Rodrigues et al. 2013;Han et al. 2014;Mo et al. 2013). Along with the arrival of
the research climax in crowdsourcing, some related international conferences such as TREC
2010 (Buckley et al. 2010) and HCOMP 2013, also organize special tracks and competitions
on crowdsourcing topics, which contribute a number of real-world data sets.
J. Zhang et al.
Tabl e 3summarizes commonly used real-world crowdsourced labeled data sets in related
researches, and provides the information of their sources, types of labels, URL and brief
describes. As the table shows, many crowdsourced labeling tasks were performed on the
traditional machine learning related data sets and tasks (Strapparava and Mihalcea 2007;
Miller and Charles 1991;Dagan et al. 2006;Pradhan et al. 2007), which facilitates the
evaluation of the proposed models and algorithms in their studies.
5.2 Open source tools
Open algorithms and data sets for public acquisition is a tendency in recent data-driven
research. Researchers in crowdsourcing also follow this tendency to involve in open source
practice (Ipeirotis et al. 2010;Whitehill et al. 2009,2010;Welinder and Perona 2010;Zhang
et al. 2014). Besides these, open source tools for the study of crowdsourced labeling also
began to emerge. Nguyen et al. (2013) proposed a tool for label integration research BATC,
which implements several ground truth inference algorithms MV, DS, RY, KOS, and GLAD.
To facilitate the evaluation of the performance of these algorithms, BATC implements a
simulation tool for crowdsourcing labeling with a visual interface. After a user sets parameters
such as the number of and the reliability levels of labelers, the simulator in BATC generates
noisy labels to form a data set, on which the integration algorithms run. Finally, an analysis
report of the running results will be presented visually with charts and tables.
Sheshadri and Lease (2013) proposed another open source tool SQUARE for the ground
truth inference study. SQUARE implements and integrates the algorithms MV, DS, RY,
GLAD, and ZenCrowd. Different from BATC, SQAURE does not provide visual analysis
and simulation tools. Instead, it provides a set of APIs that facilitate users to integrate its
function in their own program. SQUARE pays more attention to analyze real-world data sets,
so it integrates ten data sets collected from real-world crowdsourcing systems.
The authors of this survey have found that the functions of both BATC and SQUARE have
limitation that cannot support different aspects of learning from crowdsourced labeled data.
Therefore, we proposed a novel open source tool CEKA (Zhang et al. 2015a) to support the
entire research process. CEKA not only contains a large number of ground truth inference
algorithms, but also involves the model learning process after inference. CEKA follows
the object-oriented design principle, which is fully compatible with a well-known machine
learning and data mining tool WEKA (Hall et al. 2009). The sample description file (suffix
.arff) and the functions of WAKE can be directly used or called in CEKA. CEKA has a more
open architecture, which makes it easy to integrate new algorithms in the future. Figure 4
demonstrates the functional differences between CEKA and the other two tools as well as
the high level architecture of CEKA.
5.3 Running examples with CEKA
As running examples, we used real-world binary and multi-class labeling data sets to evaluate
the accuracy of several ground truth inference algorithms. These data sets and algorithms
havealreadyintegratedinCEKA.Table4lists the comparison results of the algorithms MV,
ZC (Demartini et al. 2012), GLAD (Whitehill et al. 2009), RY Raykar et al. (2010), DS
(Dawid and Skene 1979), and PLAT (Zhang et al. 2015c) on nine binary labeling data sets.
The worst results are in italic. If there are multiple worst results, the figures are underlined.
The best results are in bold. If there are multiple best results, the figures are also underlined.
According to the results, we have following observations. (1) Among the four EM-based
algorithms, RY and DS have better performance, which suggests DS is a successful model
Learning from crowdsourced labeled data: a survey
Tab l e 3 Open accessible real-world crowdsourced labeled data sets
Data set Source Label type (#C) URL Statistics (#E; #W; #L) Descriptions
Affect Snow et al. (2008) Real [0–10]
1000; 10; 7000 Judgment of seven emotions based on
SemEval Task-14 Strapparava and
Mihalcea (2007)
WordSim Real [0–10] 30; 10; 300 word semantic similarity rating Miller and
Charles (1991)
RTE Binary 800; 164; 8000 Recognize text entailment between two
sentences Dagan et al. (2006)
TempOrder Binary 462; 76; 4620 Judge the order of two events happening
(verbs) in a sentence
WSD Multiclass (3) 177; 10; 1770 Word sense disambiguation Pradhan et al.
AC2 Ipeirotis et al. (2010) Multiclass (4)
333; 269; 3317 Judge whether a webpage contains adult
materials, rating (G, PG, R, X)
Spam Binary 100; 150; 2297 Judge whether an HIT is a spam
BM Mozafari Binary
1000; 83; 5000 Judge negative/positive classes of 1000
WVSCM Whitehill et al. (2009) Binary
159; 17; 1150 Judge whether a smile face in an image is a
Duchenne smile
Duck Welinder et al. (2010) Binary https:// 240; 53; 9600 Judge whether ducks exist in the pictures
Trec2010 TREC 2010 Buckley et al.
Multiclass (5)
20,232; 766; 98,453 judge the relevance of documents, including
five classes: strong relevant, relevant, not
relevant, unlabeled and link break
FEJ HCOMP 2013 Multiclass (3)
576; 48; 2902 Judge whether statements in Wikipedia are
truth, including three classes: yes, no and
SAJ Multiclass (5)
sentiment-analysis- judgment-data
300; 461; 1720 judge the sentiment of tweets (five classes)
J. Zhang et al.
Tab l e 3 continued
Data set Source Label type (#C) URL Statistics (#E; #W; #L) Descriptions
Sentiment Rodrigues et al. (2013) Binary
5000; 203; 27,747 Judge the polarity of movie reviews
MusicGenre Multiclass () 700; 44; 2946 Classify an audio clip into one of ten
Age Han et al. (2014) Real [0–100]
1002; 165; 10,020 Estimate the age of a face in a picture
WebSearch Microsoft Multiclass (5) http://
2665; 177; 15,567 Judge the relevance of URLs based
on five relevance levels
Dog Multiclass (4) 807; 52; 7354 Classify dogs into one of four breeds
GenderHobby Mo et al. (2013) Binary http://
200; 40; 4200 Transfer knowledge between two
domains (sport and make-up)
#E,#W,#L,and#Crepresent the number of instances, the number of workers, the number of labels and the number of categories. If the type of a category is real, #Crepresents
its range
Learning from crowdsourced labeled data: a survey
Fig. 4 The architecture of open source tool CEKA (Zhang et al. 2015a)
Tab l e 4 Comparison results in
accuracy (percentage) on nine
real-world binary labeling data
Fear 75.0 75.0 75.0 79.0 80.0 80.0
Joy 66.0 66.0 66.0 77.0 75.0 75.0
Sadness 76.0 77.0 74.0 82.0 83.0 81.0
Anger 70.0 70.0 70.0 78.0 79.0 85.0
Adult 84.4 84.4 84.4 88.0 84.4 87.1
Wor d Si m 90.0 86.7 86.7 86.7 90.0 90.0
Trec10 64.2 58.8 57.8 67.8 69.2 64.6
Duck 68.8 58.8 59.6 60.0 60.8 76.7
BM 49.7 49.8 49.3 50.3 50.7 51.4
Average 71.6 69.6 69.2 74.3 74.7 76.7
for label inference. Although RY is a Bayesian version of DS, it is not always better than
DS. (2) In most cases, PLAT has the best performance, because labeling bias is a common
phenomenon in crowdsourcing. PLAT is specially proposed for handling the bias, but for
unbiased labeling cases it has the same accuracy level as MV does.
For multi-class labeling, we evaluate the algorithms MV, ZC (Demartini et al. 2012), DS
(Dawid and Skene 1979), Spectral DS (SDS) (Zhang et al. 2014)andCTIC(Zhang et al.
2016) on nine real-world data sets. From the results shown in Table 5, we have following
observations. (1) DS is still a good model for multi-class labeling. It performs much better than
ZC, since it has a more complicated model (a confusion matrix for each labeler). (2) Although
MV is the simplest method, its performance is not always poor. (3) Spectral DS, an upgraded
version of DS, performs worst in average. Spectral DS initializes the parameters of DS using
J. Zhang et al.
Tab l e 5 Comparison results in
accuracy (percentage) on nine
real-world multi-class labeling
data sets
Fej2013 90.28 90.10 87.67 92.18 90.76
Trec2010 44.20 30.98 50.27 47.59 45.48
AC2 75.68 75.98 73.57 66.06 77.78
Synth4 80.25 66.75 68.25 77.33 81.30
Valence5 36.00 32.00 40.00 45.00 54.00
Aircrowd6 80.10 81.45 76.90 56.70 81.96
Valence7 20.00 9.00 29.00 19.90 32.00
Leaves9 90.53 90.81 91.92 91.73 92.20
Leaves16 60.42 60.27 61.01 31.10 61.46
Average 64.16 59.70 64.28 58.62 68.55
a spectral method to prevent trapping into a local optimum. However, it outperforms DS
only on three data sets. Especially, as the number of classes increases, Spectral DS performs
worse. (4) GTIC has the best performance in average. Maybe it has the ability to cluster
examples with similar feature patterns (potentially belonging to one class) together.
6 Conclusions and prospects
We are now in the era of big data. The importance of knowledge discovery from massive data
is unprecedented emphasized. In a large number of intelligent systems, the learning models
are continuously optimized as the accumulation of the data. Constructing great learning
models is one of the most maturely developed branches in machine learning (Wen e t al.
2015). Although it has been widely used, it still suffers from the deficiency of the training
data. The emergence of crowdsourcing has greatly alleviated the contradiction between the
demand of the learning model and the deficiency of the training data. We can obtain plenty
of labeled data from crowdsourcing systems. However, the uncertainty of labelers derived
from the open nature of the systems is a severe challenge to the quality of collected data and
learning models.
In this paper, based on the concepts of the label quality and the learning model quality,
we elaborate the relationship between ground truth inference and building learning model.
Then, we review the inference and the learning algorithms proposed in the past eight years
and discuss the similarities and differences among these methods. Finally, in order to promote
the research in this field, we also summarize open accessible real-world crowdsourced data
sets as well as open source tools. Since 2008, in a short span of less than ten years, a lot of
new algorithms and models have burst out. However, it is still in a young stage. The problems
that are worthy of studying in the future at least include the following aspects.
1. More fine-grained inference methods At present, the research on the general purpose
ground truth algorithms is gradually mature. Various models have been able to describe
the system from different aspects. However, empirical studies have shown that due to the
huge difference among data, there is no algorithm that are universally superior to others
(Sheshadri and Lease 2013). Therefore, in order to further improve the accuracy of the
inference, more fine-grained methods need to be further studied. For example, we should
consider the historical information of labelers, and the physical features of examples,
and assign different weighs to labelers according to application domains.
Learning from crowdsourced labeled data: a survey
2. More learning models The current researches on ground truth inference and learning
model training are mainly focus on binary labeling. Although quit a few models are
claimed that they are suitable for multi-class cases, no sufficient empirical study consoli-
dates their arguments. Effective multi-class and multi-label researches in crowdsourcing
are limited (Vempaty et al. 2014;Bragg et al. 2013). In multi-class labeling, the sparsity
of data and of labels cannot be ignored. The biased labeling issue is more complicated.
Similarly, the study of multi-label learning in crowdsourcing is a brand new topic. A
series of issues, such as how to reduce the negative impact of the sparsity, need to be
studied in the future.
3. More deeply relationship between the label quality and the learning model quality
Improving the qualities of labels is conducive to improve the quality of learning models.
However, they are not equivalent. To find more valuable examples and accurately label
them is propitious to make a trade-off between the cost and the quality of a leaning
model. At the same time, a large number of practical problems are cost sensitive (Ling
and Sheng 2010). It is more useful to make a two-way optimization between labeling
cost and mislabel cost in a cost-sensitive context.
4. Theories of learning from crowdsourced labeled data Current proposed algorithms on
ground truth inference and learning model training are based on existing theories and
techniques, such as probabilistic graphical model, EM algorithm, convex optimization
and matrix singular value decomposition. Although massive empirical studies have shown
the effectiveness of these techniques, there still remain some basic theoretical questions
unsolved, for example, the upper and lower bounds of the accuracy of an inference algo-
rithm based on EM, overfitting and the way to avoid it, the impact of the size of training
data on the performance, and so on. Uncertainty in crowdsourcing makes theoretical
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... From then, during the past thirteen years, researchers in the AI community have developed many techniques to tackle the defects of using crowdsourcing in machine learning. A large number of studies focused on general-purpose technologies of learning from crowdsourced annotated data [10], including statistical truth inference [11], predictive model training with noisy labels [7], [12], [13], [14], optimization for cost-effectiveness trade-off [15], [16], [17], etc. ...
... Besides quality control, Chittilappilly et al. [19] comprehensively reviewed a wider work including incentive design and task assignment. Another previous survey [10] focused on both truth inference and predictive model learning while article [11] merely focused on truth inference. However, several years have passed since these reviews were published, and more new technologies have emerged recently. ...
Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.
... Finally, living in the era of deep learning, we must acknowledge the fact that such models require significantly higher amounts of labeled data than their shallow counterparts [45]. This has lead to a significant growth of labeling services [46]. If we encounter such problems while working with offline models, then we should expect them to be even more severe in online systems. ...
... Most popular strategies use adaptive thresholds on classifier certainty (or support functions) [15] or density information [56]. The problem of obtaining class labels becomes even more difficult when dealing with imbalanced class distributions in streams [46,57]. All of the existing active learning strategies are significantly impaired when the label query budget (i.e., the number of instances allowed to be labeled) is small. ...
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Continual learning from streaming data sources becomes more and more popular due to the increasing number of online tools and systems. Dealing with dynamic and everlasting problems poses new challenges for which traditional batch-based offline algorithms turn out to be insufficient in terms of computational time and predictive performance. One of the most crucial limitations is that we cannot assume having an access to a finite and complete data set – we always have to be ready for new data that may complement our model. This poses a critical problem of providing labels for potentially unbounded streams. In real world, we are forced to deal with very strict budget limitations, therefore, we will most likely face the scarcity of annotated instances, which are essential in supervised learning. In our work, we emphasize this problem and propose a novel instance exploitation technique. We show that when: (i) data is characterized by temporary non-stationary concepts, and (ii) there are very few labels spanned across a long time horizon, it is actually better to risk overfitting and adapt models more aggressively by exploiting the only labeled instances we have, instead of sticking to a standard learning mode and suffering from severe underfitting. We present different strategies and configurations for our methods, as well as an ensemble algorithm that attempts to maintain a sweet spot between risky and normal adaptation. Finally, we conduct a complex in-depth comparative analysis of our methods, using state-of-the-art streaming algorithms relevant for the given problem.
... Table 6 shows the detailed information of two data sets. Leaves6 [32] is a traditional classification data set which can be downloaded from CEKA. The task of data set Leaves6 is to distinguish six types of leaf pictures by judging their shapes and other characteristics. ...
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In crowdsourcing system, each instance will be usually labeled multiple times by different workers. After obtaining the multiple noise labels of data, ground truth inference algorithms are used to infer unknown true labels of instances. However, most existing ground truth inference algorithms only utilize the information in the multiple noise labels themselves while ignoring the instance similarity. This paper proposes a novel ground truth inference algorithm based on instance similarity to further improve the performance of ground truth inference. Because similar instances are more likely to be clustered in the same cluster and similar instances are more likely to have similar labels, both fuzzy c-means clustering (FCM) and k-nearest neighbors algorithm (kNN) are used to explore the instance similarity in this paper. Specifically, FCM is firstly used to adjust label distributions of instances. Then the labels of instances are inferred according to their label distributions and kNN algorithm. Based on the instance similarity, the instances with reliable label distributions will influence the instances with unreliable label distributions. The experimental results on benchmark and real-world data sets validate that using the instance similarity can effectively enhance the performance of ground truth inference.
... For example, in medicine, second opinions have been shown valuable for establishing diagnoses and initiating treatment [Burger et al., 2020] as well as reducing the number of unnecessary procedures [Leape, 1989, Althabe et al., 2004. In machine learning, ground truth labels are determined by carefully aggregating multiple noisy labels provided by different experts [Zhang et al., 2016] and inconsistencies between these noisy labels help developing more robust models [Peterson et al., 2019]. Unfortunately, the timeliness and quality of the decisions is often compromised due to a shortage of experts, which prevents each decision to be informed by multiple experts' opinions. ...