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Multiclassification by Double-Negative Aggregation of SVM Membership
Hidetoshi Tanaka
Information Technology R&D Center, Mitsubishi Electric Corporation, 5-1-1 Ofuna, Kamakura, Japan
Received:April 11, 2005Accepted:May 24, 2005Published:November 20, 2005
Keywords:support vector machine, auto target recognition, membership function, multiclassification
Abstract
Multiclassification problems are often binarized into pairwise classifications to use basic classification such as support vector machines (SVM). Instead of the widely used aggregation by fuzzy logical product, we propose simple double-negative aggregation, in which the membership functions use margin areas of SVM discrimination functions, and memberships of negative votes of the class are accumulated to produce the negative membership of the class. This provides results consistent with basic pairwise memberships, enumerates candidates when the total membership of multiple classes is nearly equal, and requires low computational cost in class reconfiguration.
Cite this article as:H. Tanaka, “Multiclassification by Double-Negative Aggregation of SVM Membership,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.6, pp. 698-707, 2005.Data files:
