Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation - Kantavat, Pittipol; Kijsirikul, Boonserm; Songsiri, Patoomsiri; Fukui, Ken-Ichi; Numao, Masayuki (Zielona Góra: Uniwersytet Zielonogórski)
We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes.
Keywords: support vector machines; multi-class classification; generalization error; entropy; decision trees