Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation

28 Aug 2017 • Pittipol Kantavat • Boonserm Kijsirikul • Patoomsiri Songsiri • Ken-ichi Fukui • Masayuki Numao

We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classi- fication. 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 classi- fier and train a new classifier for use in the classification phase... (read more)

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Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation

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