Extraction of fuzzy rules from support vector machines

被引：37
|
作者：
Castro, J. L.
Flores-Hidalgo, L. D.
Mantas, C. J. [1 ]
Puche, J. M.
机构：
[1] Univ Granada, Dept Comp Sci & AI, E-18071 Granada, Spain
[2] Cent Univ Venezuela, Sch Math, Caracas, Venezuela
来源：
FUZZY SETS AND SYSTEMS | 2007年 / 158卷 / 18期
关键词：
support vector machines; fuzzy rule-based systems; uninorms;
D O I：
10.1016/j.fss.2007.04.014
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The relationship between support vector machines (SVMs) and Takagi-Sugeno-Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for every used kernel function. Restricted methods to extract rules from SVMs have been previously published. Their limitations are surpassed with the presented extraction method. The behavior of SVMs is explained by means of fuzzy logic and the interpretability of the system is improved by introducing the lambda-fuzzy rule-based system (lambda-FRBS). The lambda-FRBS exactly approximates the SVM's decision boundary and its rules and membership functions are very simple, aggregating the antecedents with uninorms as compensation operators. The rules of the lambda-FRBS are limited to two and the number of fuzzy propositions in each rule only depends on the cardinality of the set of support vectors. For that reason, the lambda-FRBS overcomes the course of dimensionality and problems with high-dimensional data sets are easily solved with the lambda-FRBS. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：2057 / 2077
页数：21
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