Structural Regular Multiple Criteria Linear Programming for Classification Problem

Abstract

Classification problem has attracted an increasing amount of interest. Various classifiers have been proposed in the last decade, such as ANNs, LDA, and SVM. Regular Multiple Criteria Linear Programming (RMCLP) is an effective classification method, which was proposed by Shi and his colleagues and have been applied to handle different real-life data mining problems. In this paper, inspired by the application potential of RMCLP, we propose a novel Structural RMCLP (called SRMCLP) method for classification problem. Unlike RMCLP, SRMCLP is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. The corresponding optimization problem of SRMCLP can be solved by a standard quadratic programming. The effectiveness of the proposed method is demonstrated via experiments on synthetic and available benchmark datasets.