Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14714
Title: Regularized multiple criteria linear programs for classification
Authors: Shi, Y
Tian, Y
Chen, X 
Zhang, P
Keywords: Classification
Data mining
Multiple criteria mathematical program
Regularized multiple criteria mathematical program
Issue Date: 2009
Publisher: Science in China
Source: Science in China. Series F, Information sciences, 2009, v. 52, no. 10, p. 1812-1820 How to cite?
Journal: Science in China. Series F, Information sciences 
Abstract: Although multiple criteria mathematical program (MCMP), as an alternative method of classification, has been used in various real-life data mining problems, its mathematical structure of solvability is still challengeable. This paper proposes a regularized multiple criteria linear program (RMCLP) for two classes of classification problems. It first adds some regularization terms in the objective function of the known multiple criteria linear program (MCLP) model for possible existence of solution. Then the paper describes the mathematical framework of the solvability. Finally, a series of experimental tests are conducted to illustrate the performance of the proposed RMCLP with the existing methods: MCLP, multiple criteria quadratic program (MCQP), and support vector machine (SVM). The results of four publicly available datasets and a real-life credit dataset all show that RMCLP is a competitive method in classification. Furthermore, this paper explores an ordinal RMCLP (ORMCLP) model for ordinal multi-group problems. Comparing ORMCLP with traditional methods such as One-Against-One, One-Against-The rest on large-scale credit card dataset, experimental results show that both ORMCLP and RMCLP perform well.
URI: http://hdl.handle.net/10397/14714
ISSN: 1009-2757
DOI: 10.1007/s11432-009-0126-5
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