Arabic text categorization system - Using Ant Colony Optimization-based feature selection

Feature subset selection (FSS) is an important step for effective text classification (TC) systems. This paper describes a novel FSS method based on Ant Colony Optimization (ACO) and Chi-square statistic. The proposed method adapted Chi-square statistic as heuristic information and the effectiveness of Support Vector Machines (SVMs) text classifier as a guidance to better selecting features for selective categories. Compared to six classical FSS methods, Our proposed ACO-based FSS algorithm achieved better TC effectiveness. Evaluation used an in-house Arabic TC corpus. The experimental results are presented in term of macro-averaging F-1 measure.