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
收藏
页码：75 / +
页数：3
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