Several multi-criteria programming methods for classification

Zhang, J; Shi, Y; Zhang, P

Several multi-criteria programming methods for classification

Zhang, J Shi, Y Zhang, P

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computers & Operations Research, 2009, 36 (3), pp. 823 - 836
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Shi, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Computers & Operations Research, 2009, 36 (3), pp. 823 - 836	en_US
dc.identifier.issn	0305-0548	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28817
dc.description.abstract	In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 42736] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 6879; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 4460], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Computers & Operations Research	en_US
dc.relation.isbasedon	10.1016/j.cor.2007.11.001	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Several multi-criteria programming methods for classification	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	36	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	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	3	en_US
pubs.volume	36	en_US

Abstract:

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 42736] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 6879; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 4460], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.

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http://hdl.handle.net/10453/28817