Supportive Instances for Regularized Multiple Criteria Linear Programming Classification

Although classification models based on multiple criteria progarmming receive many attentions in recent years, their essence of taking every training instances into considering makes them too senstive to noisy or imbalanced samples. To overcome this shortage, in this paper, we propose a clusering based algorithm to find the representative (also called supportive) instances for a most recent multiple criteria programming classification model, the Rregularized Multiple Criteria Linear Programming (RMCLP) model, just as Support Vector Machine (SVM) finding the support vectors. Our new algorithm selects instances which locate around the clustering center as the supportive instances, and then bulid RMCLP model using only these supportive instancs. Experimental results on synthetic and real-life datasets show that our method not only improves the performance of RMCLP, but also reduces the number of training instances, which can significantly save costs in business world because labeling training samples is usually expensive and sometimes impossible.