Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification

Publisher:
Elsevier
Publication Type:
Journal Article
Citation:
Lecture Notes in Computer Science, 2008, 5102 (1), pp. 436 - 440
Issue Date:
2008-01
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Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification in data mining. Taking account of every training instance, RMCLP is sensitive to the outliers. In this paper, we propose a sample selection method to seek the representative points for RMCLP model, just as finding the support vectors to support vector machine (SVM). This sample selection method also can exclude the outliers in training set and reduce the quantity of training samples, which can significantly save costs in business world because labeling training samples is usually expensive and sometimes impossible. Experimental results show our method not only reduces the quality of training instances, but also improves the performance of RMCLP.
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