Vague one-class learning for data streams

Zhu, X; Wu, X; Zhang, C

Vague one-class learning for data streams

Zhu, X Wu, X Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2009, pp. 657 - 666
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2009, pp. 657 - 666	en_US
dc.identifier.isbn	9780769538952	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17494
dc.description.abstract	In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any time. To solve this problem, we propose a Vague One-Class Learning (VOCL) framework which employs a double weighting approach, at both instance and classifier levels, to build an ensembling framework for learning. At instance level, both local and global filterings are considered for instance weight adjustment. Two solutions are proposed to take instance weight values into the classifier training process. At classifier level, a weight value is assigned to each classifier of the ensemble to ensure that learning can quickly adapt to users' interests. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL framework significantly outperforms other methods for vaguely labeled one-class data streams. © 2009 Crown Copyright.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2009.70	en_US
dc.title	Vague one-class learning for data streams	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Miami, Florida	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any time. To solve this problem, we propose a Vague One-Class Learning (VOCL) framework which employs a double weighting approach, at both instance and classifier levels, to build an ensembling framework for learning. At instance level, both local and global filterings are considered for instance weight adjustment. Two solutions are proposed to take instance weight values into the classifier training process. At classifier level, a weight value is assigned to each classifier of the ensemble to ensure that learning can quickly adapt to users' interests. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL framework significantly outperforms other methods for vaguely labeled one-class data streams. © 2009 Crown Copyright.

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http://hdl.handle.net/10453/17494