چکیده (انگلیسی):

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.