A novel hybrid ACO-GA algorithm for text feature selection - Basiri, M.E.; Nemati, S. (2009 IEEE Congress on Evolutionary Computation, 2009)
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.