Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Bias-Variance Analysis for Ensembling Regularized Multiple Criteria Linear Programming Models
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Kernel Based Regularized Multiple Criteria Linear Programming Model
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
International Journal of Data Warehousing and Mining
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Although multiple criteria mathematical programs (MCMP), as alternative methods of classification, have been used in various real-life data mining problems, its mathematical structure of solvability are still challenge- able. This paper proposes a regularized multiple criteria linear program (RMCLP) for classification. It first adds some regularization terms in the objective function of the known multiple criteria linear program (MCLP) model for possible existence of solution. Then the paper describes the mathematical framework of the solvability. Finally, a series of experimental tests are conducted to illustrate the perfor- mance of the proposed RMCLP with the existing methods: MCLP, multiple criteria quadratic program (MCQP), and support vector machine (SVM). The results of four publicly available datasets and a real-life credit dataset all show that RMCLP is a competitive method in classification.