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Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization

Author

Listed:
  • Mohamed Abd Elaziz

    (Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13518, Lebanon)

  • Ahmed A. Ewees

    (Department of Computer, Damietta University, Damietta 34517, Egypt)

  • Mohammed A. A. Al-qaness

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • 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)

  • Rehab Ali Ibrahim

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

Abstract

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments.

Suggested Citation

  • Mohamed Abd Elaziz & Ahmed A. Ewees & Mohammed A. A. Al-qaness & Samah Alshathri & Rehab Ali Ibrahim, 2022. "Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization," Mathematics, MDPI, vol. 10(23), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4565-:d:991338
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    Citations

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    Cited by:

    1. Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.

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