A improved binary tree SVM multi-class classification algorithm is proposed. Firstly, constructing the minimum hyper ellipsoid for each class sample in the feather space, and then generating optimal binary tree according to the hyper ellipsoid volume, training sub-classifier for every non-leaf node in the binary tree at the same time. For the sample to be classified, the sub-classifiers are used from the root node until one leaf node, and the corresponding class of the leaf node is the class of the sample. The experiments are done on the Statlog database, and the experimental results show that the algorithm improves classification precision and classification speed, especially in the situation that the number of class are more and their distribution area are equal approximately, the algorithm can greatly improve the classification precision and classification speed.
