Improving knowledge extraction of Hadith classifier using decision tree algorithm

Decision tree algorithms have the ability to deal with missing values. While this ability is considered to be advantage, the extreme effort which is required to achieve it is considered a drawback. With the missing values the correct branch could be missed. Therefore, enhanced mechanisms must be employed to handle these values. Moreover, ignoring these null values may cause critical decision to user. Especially for the cases that belong to religion. The present study proposed Hadith classifier which is a method to classify such Hadith into four major classes Sahih, Hasan, Da´ef and Maudo´ according to the status of its Isnad (narrators chain). This research provided a novel mechanism to deal with missing data in Hadith database. The experiment applied C4.5 algorithm to extract the rules of classification. The findings showed that the accurate rate of the naïvebyes classifier has been improved by the proposed approach with 46.54%. Meanwhile, DT classifier had achieved 0.9% better than naïvebyes classifier.