ARABIC TEXT CATEGORIZATION SYSTEM - Using Ant Colony Optimization-based Feature Selection

Abdelwadood Moh’d A. Mesleh, Ghassan Kanaan

2008

Abstract

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 F1 measure.

Paper Citation

in Harvard Style

Moh’d A. Mesleh A. and Kanaan G. (2008). ARABIC TEXT CATEGORIZATION SYSTEM - Using Ant Colony Optimization-based Feature Selection . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-51-7, pages 384-387. DOI: 10.5220/0001892803840387

in Bibtex Style

@conference{icsoft08,
author={Abdelwadood Moh’d A. Mesleh and Ghassan Kanaan},
title={ARABIC TEXT CATEGORIZATION SYSTEM - Using Ant Colony Optimization-based Feature Selection},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2008},
pages={384-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001892803840387},
isbn={978-989-8111-51-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - ARABIC TEXT CATEGORIZATION SYSTEM - Using Ant Colony Optimization-based Feature Selection
SN - 978-989-8111-51-7
AU - Moh’d A. Mesleh A.
AU - Kanaan G.
PY - 2008
SP - 384
EP - 387
DO - 10.5220/0001892803840387