The Best Separating Decision Tree Twin Support Vector Machine for Multi-Class Classification

Abstract

In this paper, we propose a decision tree twin support vector machine (DTTSVM) for multi-class classification. To realize our DTTSVM, there are two main steps: (1), a binary tree is constructed based on the best separating principle, which maximizing the distance between the classes. (2), in our binary tree, the binary TWSVM decision model is built for each node to obtain our DTTSVM. By using the decision tree model, our DTTSVM effectively overcomes the possible ambiguous occurred in multi- TWSVM and MBSVM. The preliminary experimental results indicate that the proposed method produces simple decision trees that generalize well with respect to multi-TWSVM and MBSVM.