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

Author

Huang, Yan-Xin ; Wang, Yan ; Zhou, Chun-Guang ; Shu-Xue Zou ; Xiao-wei Yang ; Liang, An-Chun

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

Volume

6

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

3402

Abstract

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.

Keywords

fuzzy systems; knowledge acquisition; learning (artificial intelligence); optimisation; support vector machines; fuzzy classification system; fuzzy rules; fuzzy sets similarity; linguistic terms reduction; particle swarm optimization; support vector machine; Computer science; Educational institutions; Fuzzy sets; Fuzzy systems; Mathematics; Optimization methods; Particle swarm optimization; Quadratic programming; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1380374

Filename

1380374