One-class-based uncertain data stream learning

Liu, B; Xiao, Y; Cao, L; Yu, PS

One-class-based uncertain data stream learning

Liu, B Xiao, Y Cao, L

Yu, PS

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011, 2011, pp. 992 - 1003
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Xiao, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Yu, PS	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011, 2011, pp. 992 - 1003	en_US
dc.identifier.isbn	9780898719925	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19164
dc.description.abstract	This paper presents a novel approach to one-class-based uncertain data stream learning. Our proposed approach works in three steps. Firstly, we put forward a local kernel-density-based method to generate a bound score for each instance, which refines the location of the corresponding instance. Secondly, we construct an uncertain one-class classifier by incorporating the generated bound score into a one-class SVM-based learning phase. Thirdly, we devise an ensemble classifier, integrated from uncertain one-class classifiers built on the current and historical chunks, to cope with the concept drift involved in the uncertain data stream environment. Our proposed method explicitly handles the uncertainty of the input data and enhances the ability of one-class learning in reducing the sensitivity to noise. Extensive experiments on uncertain data streams demonstrate that our proposed approach can achieve better performance and is highly robust to noise in comparison with state-of-the-art one-class learning method. Copyright © SIAM.	en_US
dc.relation.ispartof	Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011	en_US
dc.title	One-class-based uncertain data stream learning	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Arizona	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:

This paper presents a novel approach to one-class-based uncertain data stream learning. Our proposed approach works in three steps. Firstly, we put forward a local kernel-density-based method to generate a bound score for each instance, which refines the location of the corresponding instance. Secondly, we construct an uncertain one-class classifier by incorporating the generated bound score into a one-class SVM-based learning phase. Thirdly, we devise an ensemble classifier, integrated from uncertain one-class classifiers built on the current and historical chunks, to cope with the concept drift involved in the uncertain data stream environment. Our proposed method explicitly handles the uncertainty of the input data and enhances the ability of one-class learning in reducing the sensitivity to noise. Extensive experiments on uncertain data streams demonstrate that our proposed approach can achieve better performance and is highly robust to noise in comparison with state-of-the-art one-class learning method. Copyright © SIAM.

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http://hdl.handle.net/10453/19164