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Authors: Abdelwadood Moh’d A. Mesleh and Ghassan Kanaan

Affiliation: Faculty of Information Systems & Technology, Arab Academy for Banking and Financial Sciences, Jordan

Keyword(s): Arabic Text Classification, Feature Selection, Ant Colony Optimization, Arabic Language, SVMs.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Natural Language Processing ; Pattern Recognition ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
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; ISSN 2184-2833, SciTePress, pages 384-387. DOI: 10.5220/0001892803840387

@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},
issn={2184-2833},
}

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
IS - 2184-2833
AU - Moh’d A. Mesleh, A.
AU - Kanaan, G.
PY - 2008
SP - 384
EP - 387
DO - 10.5220/0001892803840387
PB - SciTePress

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