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Artificial Intelligence Review

Erschienen in:

01.12.2016

Learning from crowdsourced labeled data: a survey

verfasst von: Jing Zhang, Xindong Wu, Victor S. Sheng

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2016

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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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Titel: Learning from crowdsourced labeled data: a survey
verfasst von: Jing Zhang
Xindong Wu
Victor S. Sheng
Publikationsdatum: 01.12.2016
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2016
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9491-9

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