Genetic Support Vector Classification and Feature Selection

Author

Mejia-Guevara, I. ; Kuri-Morales, Ángel

Author_Institution

Inst. de Investig. en Mat. Aplic. y Sist., Univ. Nac. Autonoma de Mexico, Mexico City

fYear

2008

fDate

27-31 Oct. 2008

Firstpage

75

Lastpage

81

Abstract

An important issue regarding the design of support vector machines (SVMs) is considered in this article, namely, the fine tuning of parameters in SVMs. This problem is tackled by using a self-adaptive genetic algorithm (GA). The same GA is used for feature selection. We validate our results implementing some statistical tests based on single domain benchmark data sets, which are used for comparison with other traditional methods. One of these methods is commonly used for the selection of parameters in SVMs.

Keywords

genetic algorithms; pattern classification; statistical testing; support vector machines; genetic support vector classification; genetic support vector feature selection; self-adaptive genetic algorithm; statistical test; support vector machines; Artificial intelligence; Benchmark testing; Circuit optimization; Genetic algorithms; Kernel; Machine learning; Static VAr compensators; Statistical analysis; Support vector machine classification; Support vector machines; Self-adaptive Genetic Algorithm; Support Vector Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, 2008. MICAI '08. Seventh Mexican International Conference on

Conference_Location

Atizapan de Zaragoza

Print_ISBN

978-0-7695-3441-1

Type

conf

DOI

10.1109/MICAI.2008.48

Filename

4682446