Genetic Support Vector Classification and Feature Selection

被引：1

作者：

Mejia-Guevaara, Ivan ^{[1
]}

Kuri-Morales, Angel ^{[2
]}

机构：

[1] Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Circuito Escolar S-N,CU, Mexico City 04510, DF, Mexico

[2] Inst Tecnol Autonoma Mexico, Dept Comp, Mexico City, DF, Mexico

来源：

PROCEEDINGS OF THE SPECIAL SESSION OF THE SEVENTH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE - MICAI 2008 | 2008年

关键词：

D O I：

10.1109/MICAI.2008.48

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 set, which are used for comparison with other traditional methods. One of these methods is commonly used for the selection of parameters in SVMs.

引用

页码：75 / +

页数：3

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