Feature selection combining genetic algorithm and Adaboost classifiers

被引：0

作者：

Chouaib, H. ^{[1
]}

Terrades, O. Ramos ^{[2
]}

Tabbone, S. ^{[2
]}

Cloppet, F. ^{[1
]}

Vincent, N. ^{[1
]}

机构：

[1] Univ Paris 05, Lab CRIP5 EA 2517, Paris, France

[2] Univ Nancy 2, LORIA, Nancy, France

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

关键词：

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：

This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.

引用

页码：3707 / 3710

页数：4

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