Abstract: Feature selection is necessary for effective text classification. Dataset preprocessing is essential to make upright result and effective performance. This paper investigates the effectiveness of using feature selection. In this paper we have been compared the performance between different classifiers in different situations using feature selection with stemming, and without stemming.Evaluation used a BBC Arabic dataset, different classification algorithms such as decision tree (D.T), K-nearest neighbors (KNN), Naïve Bayesian (NB) method and Naïve Bayes Multinomial(NBM) classifier were used. The experimental results are presented in term of precision, recall, F-Measures, accuracy and time to build model.

Ghazi Raho, Riyad Al-Shalabi, Ghassan Kanaan and Asmaa Nassar. “Different Classification Algorithms Based on Arabic Text Classification: Feature Selection Comparative Study”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.2 (2015). http://dx.doi.org/10.14569/IJACSA.2015.060228

@article{Raho2015,
title = {Different Classification Algorithms Based on Arabic Text Classification: Feature Selection Comparative Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060228},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060228},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {2},
author = {Ghazi Raho and Riyad Al-Shalabi and Ghassan Kanaan and Asmaa Nassar}
}