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Paper details
Number 4 - December 2018
Volume 28 - 2018
Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation
Pittipol Kantavat, Boonserm Kijsirikul, Patoomsiri Songsiri, Ken-Ichi Fukui, Masayuki Numao
Abstract
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. We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.
Keywords
support vector machine, multi-class classification, generalization error, entropy, decision tree