Label augmented and weighted majority voting for crowdsourcing

Crowdsourcing provides an efficient way to obtain multiple noisy labels from different crowd workers for each unlabeled instance. Label integration methods are designed to infer the unknown true label of each instance from its multiple noisy label set. We argue that when the label quality is higher...

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Veröffentlicht in:	Information sciences Jg. 606; S. 397 - 409
Hauptverfasser:	Chen, Ziqi, Jiang, Liangxiao, Li, Chaoqun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.08.2022
Schlagworte:	Crowdsourcing learning Label augmentation Label integration Label weighting Majority voting Label integration Label weighting Label augmentation Crowdsourcing learning Majority voting
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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