A Novel Hybrid ACO-GA Algorithm for Text Feature Selection

被引：23

作者：

Basiri, Mohammad Ehsan ^{[1
]}

Nemati, Shahla ^{[2
]}

机构：

[1] Univ Isfahan, Dept Comp Engn, Hezar Jerib Ave, Esfahan, Iran

[2] Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 841568311, Iran

来源：

2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5 | 2009年

关键词：

D O I：

10.1109/CEC.2009.4983263

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In our previous work we have proposed an ant colony optimization (ACO) algorithm for feature selection. In this paper, we hybridize the algorithm with a genetic algorithm (GA) to obtain excellent features of two algorithms by synthesizing them. Proposed algorithm is applied to a challenging feature selection problem. This is a data mining problem involving the categorization of text documents. We report the extensive comparison between our proposed algorithm and three existing algorithms - ACO-based, information gain (IG) and CHI algorithms proposed in the literature. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. Experimentations are carried out on Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.

引用

下载

页码：2561 / +

页数：2

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