Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis

Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, so that the most separable classes would be separated at each node of decision tree. The results of numerical simulations conducted on three datasets compared with ldquoone-against-allrdquo and ldquoone-against-onerdquo, show that the proposed method has better performance and higher generalization ability than the two conventional methods.