Regularized multiple-criteria linear programming with universum and its application

Abstract

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Universum-regularized multiple-criteria linear programming (called \({\mathfrak{U}}\)-RMCLP) was proposed and firstly applied to railway safety field, which is useful extension of RMCLP. Experiments in public datasets show that \({\mathfrak{U}}\)-RMCLP can get better results than its original model. Furthermore, experiment results in the trouble of moving freight car detection system (TFDS) datasets indicate that the accuracy of \({\mathfrak{U}}\)-RMCLP has been up to 91 %, which will provide great help for TFDS system.