A fuzzy integral method of applying support vector machine for multi-class problem

Authors:
Yanning Zhang;Hejin Yuan;Jin Pan;Ying Li;Runping Xi;Lan Yao
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China;Lab of Network Security and Countermeasure, Xi'an Communication Institute, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

This paper proposed a novel method of applying support vector machine for multi-class problem based on fuzzy integral. Firstly, the fuzzy measure of each binary classifier is constructed based on its classification accuracy during training and its agreement degrees to other support vector machines. Then the testing instances are classified by calculating the fuzzy integral between the fuzzy measures and the outputs of the binary support vector machines. The experiment results on iris and glass datasets from UCI machine learning repository and real plane dataset show that the new method is effective. And the experiment results ulteriorly indicate that the method with Choquet fuzzy integral has better performance than that with Sugeno integral.