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DOI: 10.14569/IJACSA.2015.060228
PDF

Different Classification Algorithms Based on Arabic Text Classification: Feature Selection Comparative Study

Author 1: Ghazi Raho
Author 2: Riyad Al-Shalabi
Author 3: Ghassan Kanaan
Author 4: Asmaa Nassar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 2, 2015.

  • Abstract and Keywords
  • How to Cite this Article
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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.

Keywords: Text Classification; Feature Selection; Arabic Text; Recall; F-Measure

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}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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