Self-Taught Active Learning from crowds

Fang, M; Zhu, X; Li, B; Ding, W; Wu, X

Self-Taught Active Learning from crowds

Fang, M Zhu, X Li, B Ding, W Wu, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2012, pp. 858 - 863
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Fang, M	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Li, B	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Wu, X	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2012, pp. 858 - 863	en_US
dc.identifier.isbn	9780769549057	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23009
dc.description.abstract	The emergence of social tagging and crowdsourcing systems provides a unique platform where multiple weak labelers can form a crowd to fulfill a labeling task. Yet crowd labelers are often noisy, inaccurate, and have limited labeling knowledge, and worst of all, they act independently without seeking complementary knowledge from each other to improve labeling performance. In this paper, we propose a Self-Taught Active Learning (STAL) paradigm, where imperfect labelers are able to learn complementary knowledge from one another to expand their knowledge sets and benefit the underlying active learner. We employ a probabilistic model to characterize the knowledge of each labeler through which a weak labeler can learn complementary knowledge from a stronger peer. As a result, the self-taught active learning process eventually helps achieve high classification accuracy with minimized labeling costs and labeling errors. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2012.64	en_US
dc.title	Self-Taught Active Learning from crowds	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brussels, Belgium	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The emergence of social tagging and crowdsourcing systems provides a unique platform where multiple weak labelers can form a crowd to fulfill a labeling task. Yet crowd labelers are often noisy, inaccurate, and have limited labeling knowledge, and worst of all, they act independently without seeking complementary knowledge from each other to improve labeling performance. In this paper, we propose a Self-Taught Active Learning (STAL) paradigm, where imperfect labelers are able to learn complementary knowledge from one another to expand their knowledge sets and benefit the underlying active learner. We employ a probabilistic model to characterize the knowledge of each labeler through which a weak labeler can learn complementary knowledge from a stronger peer. As a result, the self-taught active learning process eventually helps achieve high classification accuracy with minimized labeling costs and labeling errors. © 2012 IEEE.

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http://hdl.handle.net/10453/23009