A fuzzy regression based support vector machine (SVM) approach to fuzzy classification

Yu Chen, Witold Pedrycz, Junzo Watada

    研究成果: Article

    8 引用 (Scopus)

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    The objective of this study is to develop a fuzzy regression model using support vector machine (SVM) to problems of classifying patterns belonging to two overlapping classes. The design of the regression model consists of two phases. Phase I uses a fuzzy linear regression to separate linearly two classes of patterns. As a result, the fuzzy linear regression may separate the feature space into three main regions, that is (a) a region occupied by patterns belonging to class 1, (b) a region occupied by patterns belonging to class 2 and (c) the region, in which we encounter a mixture of the patterns belonging to the two classes. In Phase 2, we develop an SVM to non-linearly separate the mixture of the patterns. It will be shown that the proposed fuzzy regression comes with a significant advantage of shortening the processing time associated with the realization of the SVM.

    元の言語English
    ページ(範囲)2355-2362
    ページ数8
    ジャーナルICIC Express Letters
    4
    発行部数6 B
    出版物ステータスPublished - 2010 12

    ASJC Scopus subject areas

    • Computer Science(all)
    • Control and Systems Engineering

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