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JACIII Vol.9 No.6 pp. 698-707
doi: 10.20965/jaciii.2005.p0698
(2005)

Paper:

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, 2005
Accepted:
May 24, 2005
Published:
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:
Hidetoshi Tanaka, “Multiclassification by Double-Negative Aggregation of SVM Membership,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.6, pp. 698-707, 2005.
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