“Hadith” is considered one of the main textual sources of Islamic legislation in the Islamic religion. Therefore, evaluation and benchmarking of hadith classification approaches is considered as an important task and a challenging at the same time due to multiple evaluation attributes and criteria conflicting to verify the reliability of the Hadith. Up to our knowledge there is no study yet coverage this particular issue. In this paper, proposes and discusses a novel methodology for evaluation and benchmarking of Al-Hadith classification approaches based on reliability groups using multi-criteria analysis. To this point, two iterations should be executed; first iteration consist of two steps; (1) discussing and investigate the hadith classification using machine learning based on various artificial intelligent models in order to create the dataset, and. (2) discuss the reliability group measures to evaluate and test Hadith classification approaches in order to create a decision matrix. Followed by the second iteration using different decision-making techniques (SAW, MEW, HAW, WSM, WPM, and TOPSIS) to benchmark the results of iteration one (i.e. Hadith classification approach). As well as, discussion use the Mean, Standard deviation, and Paired sample t-test to measure the correlations among different techniques on ranking results for this case study.
Keywords:
Multi-Criteria Analysis, Evaluation and Benchmarking, Multi-Criteria Decision Making Techniques.
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