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- Title
Machine learning from crowds: A systematic review of its applications.
- Authors
G. Rodrigo, Enrique; Aledo, Juan A.; Gámez, José A.
- Abstract
Crowdsourcing opens the door to solving a wide variety of problems that previously were unfeasible in the field of machine learning, allowing us to obtain relatively low cost labeled data in a small amount of time. However, due to the uncertain quality of labelers, the data to deal with are sometimes unreliable, forcing practitioners to collect information redundantly, which poses new challenges in the field. Despite these difficulties, many applications of machine learning using crowdsourced data have recently been published that achieved state of the art results in relevant problems. We have analyzed these applications following a systematic methodology, classifying them into different fields of study, highlighting several of their characteristics and showing the recent interest in the use of crowdsourcing for machine learning. We also identify several exciting research lines based on the problems that remain unsolved to foster future research in this field. This article is categorized under: Technologies > Machine LearningApplication Areas > Science and TechnologyFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Thanks to crowdsourcing platforms, several problems that seemed impossible to tackle from a machine learning perspective, can now be solved. To discover how crowdsourced data is used as well as what are the main problems derived from its use, we analyze many applications in several fields of interest.
- Subjects
MACHINE learning; CROWDSOURCING; DATA mining; INFORMATION retrieval; DATA analysis
- Publication
WIREs: Data Mining & Knowledge Discovery, 2019, Vol 9, Issue 2, pN.PAG
- ISSN
1942-4787
- Publication type
Article
- DOI
10.1002/widm.1288