Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification

Zhang, P; Tian, Y; Li, X; Zhang, Z; Shi, Y

Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification

Zhang, P

Tian, Y Li, X Zhang, Z Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2008, 5102 (1), pp. 436 - 440
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Tian, Y	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2008, 5102 (1), pp. 436 - 440	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28908
dc.description.abstract	Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification in data mining. Taking account of every training instance, RMCLP is sensitive to the outliers. In this paper, we propose a sample selection method to seek the representative points for RMCLP model, just as finding the support vectors to support vector machine (SVM). This sample selection method also can exclude the outliers in training set and reduce the quantity of training samples, which can significantly save costs in business world because labeling training samples is usually expensive and sometimes impossible. Experimental results show our method not only reduces the quality of training instances, but also improves the performance of RMCLP.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-540-69387-1_49	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	5102	en_US
utslib.for	0803 Computer Software	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	5102	en_US

Abstract:

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification in data mining. Taking account of every training instance, RMCLP is sensitive to the outliers. In this paper, we propose a sample selection method to seek the representative points for RMCLP model, just as finding the support vectors to support vector machine (SVM). This sample selection method also can exclude the outliers in training set and reduce the quantity of training samples, which can significantly save costs in business world because labeling training samples is usually expensive and sometimes impossible. Experimental results show our method not only reduces the quality of training instances, but also improves the performance of RMCLP.

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http://hdl.handle.net/10453/28908