Dynamic classifier ensemble for positive unlabeled text stream classification

Pan, S; Zhang, Y; Li, X

Dynamic classifier ensemble for positive unlabeled text stream classification

Pan, S

Zhang, Y Li, X

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Publication Type:: Journal Article
Citation:: Knowledge and Information Systems, 2012, 33 (2), pp. 267 - 287
Issue Date:: 2012-11-01

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Field	Value	Language
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2012-11-01	en_US
dc.identifier.citation	Knowledge and Information Systems, 2012, 33 (2), pp. 267 - 287	en_US
dc.identifier.issn	0219-1377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115344
dc.description.abstract	Most of studies on streaming data classification are based on the assumption that data can be fully labeled. However, in real-life applications, it is impractical and time-consuming to manually label the entire stream for training. It is very common that only a small part of positive data and a large amount of unlabeled data are available in data stream environments. In this case, applying the traditional streaming algorithms with straightforward adaptation to positive unlabeled stream may not work well or lead to poor performance. In this paper, we propose a Dynamic Classifier Ensemble method for Positive and Unlabeled text stream (DCEPU) classification scenarios. We address the problem of classifying positive and unlabeled text stream with various concept drift by constructing an appropriate validation set and designing a novel dynamic weighting scheme in the classification phase. Experimental results on benchmark dataset RCV1-v2 demonstrate that the proposed method DCEPU outperforms the existing LELC (Li et al. 2009b), DVS (with necessary adaption) (Tsymbal et al. in Inf Fusion 9(1):56-68, 2008), and Stacking style ensemble-based algorithm (Zhang et al. 2008b). © 2011 Springer-Verlag London Limited.	en_US
dc.relation.ispartof	Knowledge and Information Systems	en_US
dc.relation.isbasedon	10.1007/s10115-011-0469-2	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Dynamic classifier ensemble for positive unlabeled text stream classification	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	33	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

Most of studies on streaming data classification are based on the assumption that data can be fully labeled. However, in real-life applications, it is impractical and time-consuming to manually label the entire stream for training. It is very common that only a small part of positive data and a large amount of unlabeled data are available in data stream environments. In this case, applying the traditional streaming algorithms with straightforward adaptation to positive unlabeled stream may not work well or lead to poor performance. In this paper, we propose a Dynamic Classifier Ensemble method for Positive and Unlabeled text stream (DCEPU) classification scenarios. We address the problem of classifying positive and unlabeled text stream with various concept drift by constructing an appropriate validation set and designing a novel dynamic weighting scheme in the classification phase. Experimental results on benchmark dataset RCV1-v2 demonstrate that the proposed method DCEPU outperforms the existing LELC (Li et al. 2009b), DVS (with necessary adaption) (Tsymbal et al. in Inf Fusion 9(1):56-68, 2008), and Stacking style ensemble-based algorithm (Zhang et al. 2008b). © 2011 Springer-Verlag London Limited.

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http://hdl.handle.net/10453/115344