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Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

Author

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  • Mohamed Abd Elaziz

    (School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    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 Artificial Intelligence Science & Engineering, Galala University, Galala 44011, Egypt)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11183, Jordan
    School of Computer Sciences, Universiti Sains Malaysia, George Town 11800, Pulau Pinang, Malaysia)

  • Dalia Yousri

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Faiyum 63514, Egypt)

  • Diego Oliva

    (Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara 44430, Mexico
    School of Computer Science & Robotics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Mohammed A. A. Al-Qaness

    (State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Mohammad H. Nadimi-Shahraki

    (Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
    Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran)

  • Ahmed A. Ewees

    (Department of Computer, Damietta University, Damietta 34511, Egypt)

  • Songfeng Lu

    (School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Technology Research Institute and Shenzhen Huazhong University of Science, Shenzhen 518057, China)

  • Rehab Ali Ibrahim

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

Abstract

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.

Suggested Citation

  • Mohamed Abd Elaziz & Laith Abualigah & Dalia Yousri & Diego Oliva & Mohammed A. A. Al-Qaness & Mohammad H. Nadimi-Shahraki & Ahmed A. Ewees & Songfeng Lu & Rehab Ali Ibrahim, 2021. "Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2786-:d:671420
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    References listed on IDEAS

    as
    1. Yi Feng & Mengru Liu & Yuqian Zhang & Jinglin Wang, 2020. "A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem," Complexity, Hindawi, vol. 2020, pages 1-19, December.
    2. Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ibrahim, Rehab Ali & Lu, Songfeng, 2020. "Opposition-based moth-flame optimization improved by differential evolution for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 168(C), pages 48-75.
    3. Mohamed Abd Elaziz & Khalid M Hosny & Ahmad Salah & Mohamed M Darwish & Songfeng Lu & Ahmed T Sahlol, 2020. "New machine learning method for image-based diagnosis of COVID-19," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
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    Cited by:

    1. Ahmed A. Ewees & Zakariya Yahya Algamal & Laith Abualigah & Mohammed A. A. Al-qaness & Dalia Yousri & Rania M. Ghoniem & Mohamed Abd Elaziz, 2022. "A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    2. Wangwang Yan & Jing Ba & Taihua Xu & Hualong Yu & Jinlong Shi & Bin Han, 2022. "Beam-Influenced Attribute Selector for Producing Stable Reduct," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    3. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.
    4. Lamiaa M. El Bakrawy & Nadjem Bailek & Laith Abualigah & Shabana Urooj & Abeer S. Desuky, 2022. "Feature Selection Based on Mud Ring Algorithm for Improving Survival Prediction of Children Undergoing Hematopoietic Stem-Cell Transplantation," Mathematics, MDPI, vol. 10(22), pages 1-19, November.

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