Descendant hierarchical support vector machine for multi-class problems

This paper presents a new approach called descendant hierarchical support vector machines (DHSVM), to treat multi-class problems. First, the method consists to build a taxonomy of classes in an ascendant manner done by ascendant hierarchical clustering method (AHC). Second, SVM is injected at each internal node of the taxonomy in order to separate the two subsets of the current node. Finally, for classifying a pattern query, we present it to the "root" SVM, and then according to the output, the pattern is presented to one of the two SVMs of the subsets, and so on through the "leaf' nodes. Therefore, the classification procedure is done in a descendant way in the taxonomy from the root through the end level which represents the classes. The pattern is thus associated to one of last SVMs associated class. AHC decomposition uses distance measures to investigate the class grouping in binary form at each level in the hierarchy. SVM method requires little tuning and yields both high accuracy levels and good generalization for binary classification. Therefore, DHSVM method gives good results for multi class problems by both, training an optimal number of SVMs and rapidly classifying patterns in a descendant way by selecting an optimal set of SVMs which participate to the final decision. The proposed method is compared to other multi-class SVM methods over several complex problems.