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An Efficient Feature Selection Method for Arabic Text Classification

by Bilal Hawashin, Ayman Mansour, Shadi Aljawarneh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 17
Year of Publication: 2013
Authors: Bilal Hawashin, Ayman Mansour, Shadi Aljawarneh
10.5120/14666-2588

Bilal Hawashin, Ayman Mansour, Shadi Aljawarneh . An Efficient Feature Selection Method for Arabic Text Classification. International Journal of Computer Applications. 83, 17 ( December 2013), 1-6. DOI=10.5120/14666-2588

@article{ 10.5120/14666-2588,
author = { Bilal Hawashin, Ayman Mansour, Shadi Aljawarneh },
title = { An Efficient Feature Selection Method for Arabic Text Classification },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 17 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number17/14666-2588/ },
doi = { 10.5120/14666-2588 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:37.391251+05:30
%A Bilal Hawashin
%A Ayman Mansour
%A Shadi Aljawarneh
%T An Efficient Feature Selection Method for Arabic Text Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 17
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an efficient, Chi-Square-based, feature selection method for Arabic text classification. In Data Mining, feature selection is a preprocessing step that can improve the classification performance. Although few works have studied the effect of feature selection methods on Arabic text classification, limited number of methods was compared. Furthermore, different datasets were used by different works. This paper improves the previous works in three aspects. First, it proposes a new efficient feature selection method for enhancing Arabic text classification. Second, it compares extended number of existing feature selection methods. Third, it adopts two publicly available datasets to encourage future works to adopt them in order to guarantee fair comparisons among the various works. Our experiments show that our proposed method outperformed the existing methods in term of accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Arabic Text Retrieval Feature Selection CHI Square.

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