A Regularized Multiple Criteria Linear Program for Classification

Yong Shi, Yingjie Tian, Xiaojun Chen, Peng Zhang

Abstract: Although multiple criteria mathematical programs (MCMP), as alternative methods of classification, have been used in various real-life data mining problems, its mathematical structure of solvability are still challenge- able. This paper proposes a regularized multiple criteria linear program (RMCLP) for classification. 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 perfor- mance 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.

External IDs:dblp:conf/icdm/ShiTCZ07