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

Yu Chen, Witold Pedrycz, Junzo Watada

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)2355-2362
    Number of pages8
    JournalICIC Express Letters
    Volume4
    Issue number6 B
    Publication statusPublished - 2010 Dec

    Keywords

    • Classification
    • Fuzzy linear regression
    • Non-linearly distributed samples
    • Support vector machine (SVM)

    ASJC Scopus subject areas

    • Computer Science(all)
    • Control and Systems Engineering

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