Genetic algorithm for feature selection with mutual information

被引：4

作者：

Ge, Hong ^{[1
]}

Hu, Tianliang ^{[1
]}

机构：

[1] South China Normal Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China

来源：

2014 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2014), VOL 1 | 2014年

关键词：

feature selection; mutual information; genetic algorithm; generalization;

D O I：

10.1109/ISCID.2014.122

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A feature selection approach combining genetic algorithm(GA) with mutual information (FSGM) is proposed. In fact, FSGM is a genetic algorithm applied to feature selection. For feature selection task, an individual of GA represents a feature subset, and the fitness function is the evaluation of the feature subset. With elaborating design, the global searching and completely evaluation can be realized in FSGM. The experimental results confirm the effectiveness of the proposed algorithm in improving the generalization and reducing the overfitting of selected feature subset.

引用

页码：116 / 119

页数：4

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