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Improved sine cosine algorithm with simulated annealing and singer chaotic map for Hadith classification

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Abstract

Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. Sine Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization Algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simultaneously improve the classification accuracy while it selects the most informative features.

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Acknowledgements

This study was funded by the University of Malaya (Grant No: UMRG RP043C-17HNE).

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Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates

    Mohammad Tubishat

  2. Department of Linguistics, Academy of Malay Studies University of Malaya, 50603, Kuala Lumpur, Malaysia

    Salinah Ja’afar & Mardian Shah Omar

  3. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Norisma Idris & Maizatul Akmar Ismail

  4. Artificial Intelligence Research Center (AIRC), Artificial Intelligence Research Center (AIRC), Department of Information Technology, MSAI College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  5. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, P.O. Box 50, Irbid, Jordan

    Mohammed Azmi Al-Betar

  6. School of Electrical and Computer Engineering (ICT), Xiamen University Malaysia, Bandar Sunsuria, 43900, Sepang, Selangor Darul Ehsan, Malaysia

    Mohammed Alswaitti

  7. School of Information Technology, Skyline University College, Sharjah, United Arab Emirates

    Hazim Jarrah

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  1. Mohammad Tubishat
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Tubishat, M., Ja’afar, S., Idris, N. et al. Improved sine cosine algorithm with simulated annealing and singer chaotic map for Hadith classification. Neural Comput & Applic 34, 1385–1406 (2022). https://doi.org/10.1007/s00521-021-06448-y

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