Optimal Decision Tree Based Multi-class Support Vector Machine

In this paper, decision tree SVMs architecture is constructed to solve multi-class problems. To maintain high generalization ability, the optimal structure of decision tree is determined using statistical measures for obtaining class separability. The proposed optimal decision tree SVM (ODT-SVM) takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVM. A robust non-parametric test is carried out for statistical comparison of proposed ODT-SVM with other classifiers over multiple data sets. Performance is evaluated in terms of classification accuracy and computation time. The statistical analysis on UCI repository datasets indicate that ten cross validation accuracy of our proposed framework is significantly better than widely used multi-class classifiers. Experimental results and statistical tests have shown that the proposed ODT-SVM is significantly better in comparison to conventional OvO and OAA in terms of both training and testing time.