Novel design of decision-tree-based support vector machines multi-class classifier

Abstract

Designing the hierarchical structure is a key issue for the decisiontree-based (DTB) support vector machines multi-class classification. Inter-class separability is an important basis for designing the hierarchical structure. A new method based on vector projection is proposed to measure inter-class separability. Furthermore, two different DTB support vector multi-class classifiers are designed based on the inter-class separability: one is in the structure of DTB-balanced branches and another is in the structure of DTB-one against all. Experiment results on three large-scale data sets indicate that the proposed method speeds up the decision-tree-based support vector machines multi-class classifiers and yields higher precision. © Springer-Verlag Berlin Heidelberg 2007.