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An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Author

Listed:
  • Ibrahim Al-Shourbaji

    (Department of Computer and Network Engineering, Jazan University, Jazan 45142, Saudi Arabia
    Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Pramod H. Kachare

    (Department of Electronics & Telecomm, Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai 400706, Maharashtra, India)

  • Samah Alshathri

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Salahaldeen Duraibi

    (Department of Computer and Network Engineering, Jazan University, Jazan 45142, Saudi Arabia)

  • Bushra Elnaim

    (Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Kharj 16278, Saudi Arabia)

  • Mohamed Abd Elaziz

    (Faculty of Science & Engineering, Galala University, Suze 435611, Egypt
    Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

Abstract

Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented to select the most Optimal Features Subset (OFS) in a given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods in a parallel mechanism to determine OFS. This mechanism decreases the chance of the two methods to stuck in local optima and it boosts the capability of both of them to balance exploration and explication. Numerous experiments are performed on ten datasets taken from the UCI repository and two real-world engineering problems to evaluate RSA-SO. The obtained results from the RSA-SO are also compared with seven popular Meta-Heuristic (MH) methods for FS to prove its superiority. The results show that the developed RSA-SO approach has a comparative performance to the tested MH methods and it can provide practical and accurate solutions for engineering optimization problems.

Suggested Citation

  • Ibrahim Al-Shourbaji & Pramod H. Kachare & Samah Alshathri & Salahaldeen Duraibi & Bushra Elnaim & Mohamed Abd Elaziz, 2022. "An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection," Mathematics, MDPI, vol. 10(13), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2351-:d:856033
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    References listed on IDEAS

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    1. Ikeda, Shintaro & Nagai, Tatsuo, 2021. "A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems," Applied Energy, Elsevier, vol. 289(C).
    2. Ibrahim Al-Shourbaji & Na Helian & Yi Sun & Samah Alshathri & Mohamed Abd Elaziz, 2022. "Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
    3. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
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