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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Vydané v:	Information sciences Ročník 606; s. 397 - 409
Hlavní autori:	Chen, Ziqi, Jiang, Liangxiao, Li, Chaoqun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.08.2022
Predmet:	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
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