Regularized estimation for preference disaggregation in multiple criteria decision making

Zhang, Y; Zhang, P; Shi, Y

Regularized estimation for preference disaggregation in multiple criteria decision making

Zhang, Y Zhang, P

Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2009, 5545 (1), pp. 625 - 632
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2009, 5545 (1), pp. 625 - 632	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28907
dc.description.abstract	Although Regularized Multiple Criteria Linear Programming (RMCLP) model has shown its effectiveness in classification problems, its inherent drawback of linear formulation limits itself into only solving linear classification problems. To extend RMCLP into solving non-linear problems, in this paper, we propose a kernel based RMCLP model by using a form w=?i=1Nßi?(xi) to replace the original weight w in RMCLP model. Empirical studies on synthetic and real-life datasets demonstrate that our new model is capable to classify non-linear datasets. Moreover, comparisons to SVM and MCQP also exhibit the fact that our new model is superior to other non-linear models in classification problems.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-01973-9_70	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Regularized estimation for preference disaggregation in multiple criteria decision making	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	5545	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	5545	en_US

Abstract:

Although Regularized Multiple Criteria Linear Programming (RMCLP) model has shown its effectiveness in classification problems, its inherent drawback of linear formulation limits itself into only solving linear classification problems. To extend RMCLP into solving non-linear problems, in this paper, we propose a kernel based RMCLP model by using a form w=?i=1Nßi?(xi) to replace the original weight w in RMCLP model. Empirical studies on synthetic and real-life datasets demonstrate that our new model is capable to classify non-linear datasets. Moreover, comparisons to SVM and MCQP also exhibit the fact that our new model is superior to other non-linear models in classification problems.

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http://hdl.handle.net/10453/28907