Reduction and optimization for a support-vector-machine-based fuzzy-classification-system

被引：0
|
作者：
Huang, YX [1 ]
Wang, Y [1 ]
Zhou, CG [1 ]
Zou, SX [1 ]
Yang, XW [1 ]
Liang, YC [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
support vector machine; fuzzy systems; rule reduction; particle swarm optimization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A fuzzy classification system model based on Support Vector Machine is proposed in this paper. Reduction methods are developed to minimize the complexity of the system by reducing the linguistic terms in the fuzzy rules based on the similarity of fuzzy sets, and removing the redundant and inconsistent fuzzy rules. Finally, the particle swarm optimization is used to adjust the system parameters for compensating the deviation caused by the reduction. Experimental results show,that the methods are feasible and effective.
引用
收藏
页码：3402 / 3407
页数：6
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