Journal of Computer Science

A COMPARATIVE STUDY OF COMBINED FEATURE SELECTION METHODS FOR ARABIC TEXT CLASSIFICATION

Aisha Adel, Nazlia Omar and Adel Al-Shabi

DOI : 10.3844/jcssp.2014.2232.2239

Journal of Computer Science

Volume 10, Issue 11

Pages 2232-2239

Abstract

Text classification is a very important task due to the huge amount of electronic documents. One of the problems of text classification is the high dimensionality of feature space. Researchers proposed many algorithms to select related features from text. These algorithms have been studied extensively for English text, while studies for Arabic are still limited. This study introduces an investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gain and Relief F. In addition, this study also introduces an approach of combination of feature selection methods based on the average weight of the features. The experiments are conducted using Naïve Bayes and Support Vector Machine classifiers to classify a published Arabic corpus. The results show that the best results were obtained when using Information Gain method. The results also show that the combination of multiple feature selection methods outperforms the best results obtain by the individual methods.

Copyright

© 2014 Aisha Adel, Nazlia Omar and Adel Al-Shabi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.