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Classifications of Hadiths based on Supervised Learning Techniques


Hammam M. AbdElaal, Belgacem Bouallegue, Motasem Elshourbagy, Safaa S. Matter, Hany A. AbdElghfar ,Mahmoud M. Khattab, ,Abdelmoty M. Ahmed


Vol. 22  No. 11  pp. 1-10


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.


Text Classification, Chi-square, MultinomialNB, KNeighbors, LinearSVC, Association rules, Hadith