Integrating Swarm Intelligence and Statistical Data for Feature Selection in Text Categorization
M. Janaki Meena, K. R. Sarath Chandran, J. Mary Brinda · International Journal of Computer Applications · 2010
Feature selection is the principal step in classification problems with attributes of high dimension.It may also be considered as a problem to determine the subset of terms in training corpus, which maximizes the classifier's performance.Most of the machine learning algorithms has tainted performance in high dimensional feature space.In this paper, a novel feature selection method based on Ant Colony Optimization, a swarm intelligence algorithm is proposed.Ant Colony Optimization is a metaheuristic algorithm used to increase the ability of finding high quality solutions to NP-hard problems.The heuristic information required for the optimization process is obtained through a chi-square based statistical method, CHIR which results in fast convergence.Performance of the classifier with features selected by proposed method is compared to the feature selected by conventional chi-square and CHIR methods.It is found that the proposed algorithm identifies better feature set than the conventional methods.