Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

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 th...

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Bibliographic Details
Main Authors: Kantavat Pittipol, Kijsirikul Boonserm, Songsiri Patoomsiri, Fukui Ken-Ichi, Numao Masayuki
Format: Article
Language:English
Published: Sciendo 2018-12-01
Series:International Journal of Applied Mathematics and Computer Science
Subjects:
support vector machine
multi-class classification
generalization error
entropy
decision tree
Online Access:https://doi.org/10.2478/amcs-2018-0054

Internet

https://doi.org/10.2478/amcs-2018-0054

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