International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Classifications of Hadiths based on Supervised Learning Techniques

  • AbdElaal, Hammam M. (Department of Information Technology, Faculty of Computers and information, Luxor University) ;
  • Bouallegue, Belgacem (College of Computer Science, King Khalid University) ;
  • Elshourbagy, Motasem (Department of Physics and Engineering Mathematics Mattaria, Faculty of Engineering, Helwan University) ;
  • Matter, Safaa S. (Department of Computer Science, Applied College, King Khalid University) ;
  • AbdElghfar, Hany A. (Department of Computers and Systems Engineering, Faculty of Engineering, Minia University) ;
  • Khattab, Mahmoud M. (College of Computer Science, King Khalid University) ;
  • Ahmed, Abdelmoty M. (College of Computer Science, King Khalid University)
  • Received : 2022.11.05
  • Published : 2022.11.30
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Abstract

This study aims to build a model is capable of classifying the categories of hadith, according to the reliability of hadith' narrators (sahih, hassan, da'if, maudu) and according to what was attributed to the Prophet Muhammad (saying, doing, describing, reporting ) using the supervised learning algorithms, with a view to discover a relationship between these classifications, based on the outputs of this model, which might be useful to avoid the controversy and useless debate on automatic classifications of hadith, using some of the statistical methods such as chi-square, information gain and association rules. The experimental results showed that there is a relation between these classifications, most of Sahih hadiths are belong to saying class, and most of maudu hadiths are belong to reporting class. Also the best classifier had given high accuracy was MultinomialNB, it achieved higher accuracy reached up to 0.9708 %, for his ability to process high dimensional problems and identifying the most important features that are relevant to target data in training stage. Followed by LinearSVC classifier, reached up to 0.9655, and finally, KNeighborsClassifier reached up to 0.9644.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups [grant number RGP.2/208/43].

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