Multi-modal tree-based SVM classification

This paper presents a method for designing binary trees for SVM classification. The proposed algorithm, multimodal binary tree (MBT) tolerates misclassification in the upper nodes of the tree, allowing points to be classified in either output regardless of the initial specified class groupings. MBT can separate classes that are inseparable with a single classifier by using a piecewise division. The algorithm also incorporates feature selection for the individual classifiers in the system. Classification results on several artificial and real data sets show that the proposed algorithm performs well compared to existing methods for multi-class SVM classification, and although the classifiers are larger, the time required to classify a point is smaller.