Support Vector Machines based Arabic Language Text Classification System: Feature Selection Comparative Study

  • Abdelwadood. Moh’d. Mesleh


feature selection (FS) is essential for effective and more accurate text classification (TC) systems. This paper investigates the effectiveness of five commonly used FS methods for our Arabic language TC System. Evaluation used an in-house collected Arabic TC corpus. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F1 measure.


Support Vector Machine Feature Selection Mutual Information Information Gain Support Vector Machine Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Abdelwadood. Moh’d. Mesleh
    • 1
  1. 1.Computer Engineering Department, Faculty of Engineering TechnologyBalqa’ Applied UniversityJordan

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