Kernel Regularized Multiple Criteria Linear Programming

Based on our proposed regularized multiple criteria linear programming (RMCLP) for binary classification problems, this paper extends this method to treat with nonlinear case. By applying dual theory, we derived the dual problem of optimization problem constructed in RMCLP, and then proved the solution of RMCLP can be computed by the solution of its dual problem, finally, we constructed Algorithm Kernel RMCLP by introducing Kernel functions in RMCLP. A series of experimental tests are conducted to illustrate the performance of the proposed Kernel RMCLP with the outstanding support vector machine (SVM). The results of several publicly available datasets and a real-life credit dataset all show that our Kernel RMCLP is a competitive method in classification.