Support vector machines ensemble based on fuzzy integral for classification

Authors:
Genting Yan;Guangfu Ma;Liangkuan Zhu
Affiliations:
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Support vector machines (SVMs) ensemble has been proposed to improve classification performance recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. A SVMs ensemble method based on fuzzy integral is presented in this paper to deal with this problem. This method aggregates the outputs of separate component SVMs with importance of each component SVM, which is subjectively assigned as the nature of fuzzy logic. The simulating results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy.