A novel hybrid ACO-GA algorithm for text feature selection

• Published in: 2009 IEEE Congress on Evolutionary Computation pp. 2561 - 2568
• Main Authors: Basiri, M.E., Nemati, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Ant colony optimization; Artificial intelligence; Data mining; Genetic algorithms; Machine learning; Machine learning algorithms; Particle swarm optimization; Signal processing algorithms; Space technology; Text categorization
• ISBN: 1424429587; 9781424429585
• ISSN: 1089-778X
• DOI: 10.1109/CEC.2009.4983263

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.