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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Bibliographic Details

Published in:	Information sciences Vol. 606; pp. 397 - 409
Main Authors:	Chen, Ziqi, Jiang, Liangxiao, Li, Chaoqun
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.08.2022
Subjects:	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 Access:	Get full text
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