Regularized multiple-criteria linear programming via second order cone programming formulations

Authors:
Zhiquan Qi;Yingjie Tian;Yong Shi
Affiliations:
Research Center on Fictitious, Economy & Data Science, Chinese Academy of Sciences, Beijing, China;Research Center on Fictitious, Economy & Data Science, Chinese Academy of Sciences, Beijing, China;Research Center on Fictitious, Economy & Data Science, Chinese Academy of Sciences, Beijing, China
Venue:
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
Year:
2012

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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. So far, current RMCLP implicitly assumes the training data to be known exactly. However, in practice, there are usually many measurement and statistical errors in the training data. In this paper, we propose a Robust Regularized Multiple-Criteria Linear Programming (called R-RMCLP) via second order cone programming formulations for classification. Preliminary numerical experiments show the robustness of our method.