A regularized multiple criteria linear program for classification

Yong Shi, Yingjie Tian, Xiaojun Chen, Peng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

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 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.
Original languageEnglish
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages253-258
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: 28 Oct 200731 Oct 2007

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/TerritoryUnited States
CityOmaha, NE
Period28/10/0731/10/07

ASJC Scopus subject areas

  • Engineering(all)

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