Class noise handling for effective cost-sensitive learning by cost-guided iterative classification filtering

Zhu, X; Wu, X

Class noise handling for effective cost-sensitive learning by cost-guided iterative classification filtering

Zhu, X Wu, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2006, 18 (10), pp. 1435 - 1440
Issue Date:: 2006-10-01

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Field	Value	Language
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Wu, X	en_US
dc.date.issued	2006-10-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2006, 18 (10), pp. 1435 - 1440	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15067
dc.description.abstract	Recent research in machine learning, data mining, and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the misclassification cost becomes the objective. These methods often assume that their input is quality data without conflict or erroneous values, or the noise impact is trivial, which is seldom the case in real-world environments. In this paper, we propose a Cost-guided Iterative Classification Filter (CICF) to identify noise for effective CS learning. Instead of putting equal weights on handling noise in all classes in existing efforts, CICF puts more emphasis on expensive classes, which makes it attractive in dealing with data sets with a large cost-ratio. (Experimental results and comparative studies indicate that the existence of noise may seriously corrupt the performance of the underlying CS learners and by adopting the proposed CICF algorithm, we can significantly reduce the misclassification cost of a CS classifier in noisy environments. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2006.155	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Class noise handling for effective cost-sensitive learning by cost-guided iterative classification filtering	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	18	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
dc.location.activity	Ballarat, Australia
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.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

Recent research in machine learning, data mining, and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the misclassification cost becomes the objective. These methods often assume that their input is quality data without conflict or erroneous values, or the noise impact is trivial, which is seldom the case in real-world environments. In this paper, we propose a Cost-guided Iterative Classification Filter (CICF) to identify noise for effective CS learning. Instead of putting equal weights on handling noise in all classes in existing efforts, CICF puts more emphasis on expensive classes, which makes it attractive in dealing with data sets with a large cost-ratio. (Experimental results and comparative studies indicate that the existence of noise may seriously corrupt the performance of the underlying CS learners and by adopting the proposed CICF algorithm, we can significantly reduce the misclassification cost of a CS classifier in noisy environments. © 2006 IEEE.

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http://hdl.handle.net/10453/15067