Affiliations: [a] Research Center on Fictitious Economy and Data Science of Chinese Academy of Sciences, Beijing, China | [b] University of Chinese Academy of Sciences, Beijing, China | [c] College of Information Science and Technology University of Nebraska at Omaha, Omaha, NE, USA | [d] Post-Doctoral Research Station of Tsinghua University, Beijing, China | [e] Post-Doctoral Research Station of China Construction Bank, Beijing, China
Correspondence:
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Corresponding author: Yong Shi, Research Center on Fictitious Economy and Data Science of Chinese Academy of Sciences, Beijing, China. Tel.: +86 010 8268 0697; Fax: +86 010 8268 0697; E-mail:[email protected]
Abstract: Handling data classification and regression problems through linear hyperplane is a naive and simple idea. In this paper, inspired by the idea of multiple criteria linear programs (MCLP) and multiple criteria quadratic programs (MCQP), we proposed a novel method for binary classification and regression problem. There are two main advantages for the proposed approach. One is that both of these two models guarantee the existence of feasible solutions when the model parameters were chosen properly. The other is that nonlinear patterns could be handled and captured by introducing kernel function into MCLP framework with a more natural way than previous work. Various classical approaches and datasets were evaluated in our experiments, and the result on both toy and real world data demonstrate the correctness and effectiveness of our proposed methods.
Keywords: Multiple criteria linear program, optimization, binary classification, regression
