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

被引：0
|
作者：
Chen, Yu [1 ]
Pedrycz, Witold [2 ,3 ]
Watada, Junzo [1 ]
机构：
[1] Graduate School of Information, Production and Systems, Waseda University, 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka-ken 808-0135, Japan
[2] Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada
[3] Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
来源：
ICIC Express Letters | 2010年 / 4卷 / 6 B期
关键词：
C (programming language) - Linear regression - Separation - Mixtures;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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. © 2010 ISSN 1881-803X.
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页码：2355 / 2362
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