Abstract:

To solve the multi-class fault diagnosis tasks, decision
tree support vector machine (DTSVM), which combines SVM
and decision tree using the concept of dichotomy, is proposed.
Since the classification performance of DTSVM highly depends on
its structure, to cluster the multi-classes with maximum distance
between the clustering centers of the two sub-classes, genetic algorithm
is introduced into the formation of decision tree, so that the
most separable classes would be separated at each node of decisions
tree. Numerical simulations conducted on three datasets
compared with “one-against-all” and “one-against-one” demonstrate
the proposed method has better performance and higher
generalization ability than the two conventional methods.