Title :
A novel hybrid ACO-GA algorithm for text feature selection
Author :
Basiri, Mohammad Ehsan ; Nemati, Shahla
Author_Institution :
Comput. Eng. Dept., Univ. of Isfahan, Isfahan
Abstract :
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.
Keywords :
data mining; genetic algorithms; text analysis; ACO-GA algorithm; CHI algorithms; ant colony optimization; data mining; genetic algorithm; information gain; text documents; text feature selection; Ant colony optimization; Artificial intelligence; Data mining; Genetic algorithms; Machine learning; Machine learning algorithms; Particle swarm optimization; Signal processing algorithms; Space technology; Text categorization;
Conference_Titel :
Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-2958-5
Electronic_ISBN :
978-1-4244-2959-2
DOI :
10.1109/CEC.2009.4983263
