Half-Against-Half multi-class support vector machines

Authors:
Hansheng Lei;Venu Govindaraju
Affiliations:
CUBS, Center for Biometrics and Sensors, Computer Science and Engineering Department, State University of New York at Buffalo, Amherst, NY;CUBS, Center for Biometrics and Sensors, Computer Science and Engineering Department, State University of New York at Buffalo, Amherst, NY
Venue:
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Year:
2005

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Abstract

A Half-Against-Half (HAH) multi-class SVM is proposed in this paper. Unlike the commonly used One-Against-All (OVA) and One-Against-One (OVO) implementation methods, HAH is built via recursively dividing the training dataset of K classes into two subsets of classes. The structure of HAH is same as a decision tree with each node as a binary SVM classifier that tells a testing sample belongs to one group of classes or the other. The trained HAH classifier model consists of at most K binary SVMs. For each classification testing, HAH requires at most K binary SVM evaluations. Both theoretical estimation and experimental results show that HAH has advantages over OVA and OVO based methods in the evaluation speed as well as the size of the classifier model while maintaining comparable accuracy.