IIUM Repository (IREP)

Novel mechanism to improve Hadith classifier performance

Aldhlan, Kawther A. and Zeki, Akram M. and Zeki, Ahmed M. and Alreshidi, Hamad (2012) Novel mechanism to improve Hadith classifier performance. In: International Conference on Advanced Computer Science Applications and Technologies , 26-28 Nov 2012, The Palace of Golden Horses- KL.

[img]
Preview
PDF
Download (313kB) | Preview

Abstract

Abstract— Muslims believe that the Sunnah of the Prophet Muhammad (SAAW) is the second of the two revealed fundamental sources of Islam, after the Holy Qur'an. Hadith provides a Gold Standard "ground truth" for Artificial Intelligent (AI) knowledge extraction and knowledge representation experiments. In the present study, the extracted Islamic knowledge represented the focal point of the research; three famous books in Hadith science framed the corpus of the study. This study attempted to explore new approach to classify Hadith using a combination of the expert system and data mining techniques to classify Hadith according to its validity degree (Sahih, Hasan, Da'eef and Maudo'), the proposed Hadith Classifier model was built through learning process, Decision Tree (DT) classifier modeling had been represented by the tree structure model, and the attributes of the instances originally were obtained from the source books. Whilst some attributes were indicated as null values, or missing values. A novel mechanism called missing data detector (MDD) was employed to handle these missing data. This mechanism simulated the Isnad verification methods in Hadith science. The results of the research were compared with the resource books, concurrently with the point of view of the experts in the Hadith science. The findings of the research showed that the performance of DT Hadith classifier had significant effect with MDD, the CCR was sharply increased from (50.1502 %) to (97.597%) Furthermore, the favorable obtained results indicated that the DT Modeling is a viable approach to classify Hadith due to the ease of rules induction and results interpretation.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 6153/30804 --- ISBN: 978-0-7695-4959-0/13, Print ISBN: 978-1-4673-5832-3
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Akram M Zeki
Date Deposited: 22 Aug 2013 15:35
Last Modified: 08 Dec 2014 15:38
URI: http://irep.iium.edu.my/id/eprint/30804

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year