改进的基于SVM决策树的多分类算法

Standard SVM is aimed at the problem of two class classification, how will the two class problems are extended to multi-class problems, is currently a hot research.In this paper a improved multi-class classification algorithm based on SVM decision tree is proposed. The decision tree is constructed based on the prior distribution of samples, which can make the more separable at the upper node of the decision tree according to the new inter-class separability. This paper design two new multi-class classification algorithms based on SVM decision tree for both unbalance and balance tree structure. The experimental result indicates that the algorithm can significantly reduce system testing time at the condition of not reducing identification rate, and is an effective multi-class classification algorithm.