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A Quantum-Based Chameleon Swarm for Feature Selection

Author

Listed:
  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon)

  • Mahmoud Ahmadein

    (Mechanical Engineering Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
    Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta 31527, Egypt)

  • Sabbah Ataya

    (Mechanical Engineering Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

  • Naser Alsaleh

    (Mechanical Engineering Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

  • Agostino Forestiero

    (Institute for High Performance Computing and Networking, National Research Council of Italy, 87036 Rende, Italy)

  • Ammar H. Elsheikh

    (Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta 31527, Egypt)

Abstract

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.

Suggested Citation

  • Mohamed Abd Elaziz & Mahmoud Ahmadein & Sabbah Ataya & Naser Alsaleh & Agostino Forestiero & Ammar H. Elsheikh, 2022. "A Quantum-Based Chameleon Swarm for Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3606-:d:931990
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    References listed on IDEAS

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    1. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
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    Cited by:

    1. Seyed Hamidreza Hazaveh & Ali Bayandour & Azam Khalili & Ali Barkhordary & Ali Farzamnia & Ervin Gubin Moung, 2023. "Impulsive Noise Suppression Methods Based on Time Adaptive Self-Organizing Map," Energies, MDPI, vol. 16(4), pages 1-15, February.

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