DocumentCode :
2916157
Title :
Application of ant colony optimization for feature selection in text categorization
Author :
Aghdam, Mehdi Hosseinzadeh ; Ghasem-Aghaee, Nasser ; Basiri, Mohammad Ehsan
Author_Institution :
Comput. Eng. Dept., Univ. of Isfahan, Esfahan
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
2867
Lastpage :
2873
Abstract :
Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features. A major problem of text categorization is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorization. This paper presents a novel feature selection algorithm that is based on ant colony optimization. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of information gain and CHI algorithms on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.
Keywords :
computational complexity; feature extraction; optimisation; text analysis; CHI algorithms; Reuters-21578 dataset; ant colony optimization; computational complexity; feature selection; text categorization; Ant colony optimization; Evolutionary computation; Length measurement; Text categorization; Weight control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631182
Filename :
4631182
Link To Document :
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