Support Vector Machine Text Classification System: Using Ant Colony Optimization Based Feature Subset Selection

Abstract

Feature subset selection (FSS) is an important step for effective text classification systems. In this work, we have implemented a support vector machine (SVM) text classifier for Arabic articles. Moreover, we have implemented a novel FSS method based on Ant Colony Optimization (ACO) and Chi-square statistic. The proposed ACO-Based FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improve the selection of features for each category. Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness. Evaluation used an in-house Arabic text classification corpus that consists of 1445 documents independently classified into nine categories. The experimental results were presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> measures.