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Learning from crowdsourced labeled data: a survey

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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 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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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Jing Zhang

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Xindong Wu

  3. Department of Computer Science, University of Central Arkansas, Conway, AR, 72035, USA

    Victor S. Sheng

  4. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, China

    Victor S. Sheng

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  1. Jing Zhang
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  3. Victor S. Sheng
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Correspondence to Jing Zhang.

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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.

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Zhang, J., Wu, X. & Sheng, V.S. Learning from crowdsourced labeled data: a survey. Artif Intell Rev 46, 543–576 (2016). https://doi.org/10.1007/s10462-016-9491-9

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