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A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem

Author

Listed:
  • Ruba Abu Khurma

    (King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan)

  • Ibrahim Aljarah

    (King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan)

  • Ahmad Sharieh

    (King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan)

  • Mohamed Abd Elaziz

    (Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Robertas Damaševičius

    (Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania)

  • Tomas Krilavičius

    (Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania)

Abstract

This survey is an effort to provide a research repository and a useful reference for researchers to guide them when planning to develop new Nature-inspired Algorithms tailored to solve Feature Selection problems (NIAs-FS). We identified and performed a thorough literature review in three main streams of research lines: Feature selection problem, optimization algorithms, particularly, meta-heuristic algorithms, and modifications applied to NIAs to tackle the FS problem. We provide a detailed overview of 156 different articles about NIAs modifications for tackling FS. We support our discussions by analytical views, visualized statistics, applied examples, open-source software systems, and discuss open issues related to FS and NIAs. Finally, the survey summarizes the main foundations of NIAs-FS with approximately 34 different operators investigated. The most popular operator is chaotic maps. Hybridization is the most widely used modification technique. There are three types of hybridization: Integrating NIA with another NIA, integrating NIA with a classifier, and integrating NIA with a classifier. The most widely used hybridization is the one that integrates a classifier with the NIA. Microarray and medical applications are the dominated applications where most of the NIA-FS are modified and used. Despite the popularity of the NIAs-FS, there are still many areas that need further investigation.

Suggested Citation

  • Ruba Abu Khurma & Ibrahim Aljarah & Ahmad Sharieh & Mohamed Abd Elaziz & Robertas Damaševičius & Tomas Krilavičius, 2022. "A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem," Mathematics, MDPI, vol. 10(3), pages 1-45, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:464-:d:739228
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    Citations

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    Cited by:

    1. Xueyu Chen & Minghua Wan & Hao Zheng & Chao Xu & Chengli Sun & Zizhu Fan, 2022. "A New Bilinear Supervised Neighborhood Discrete Discriminant Hashing," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    2. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    3. Abrar Yaqoob & Rabia Musheer Aziz & Navneet Kumar Verma & Praveen Lalwani & Akshara Makrariya & Pavan Kumar, 2023. "A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification," Mathematics, MDPI, vol. 11(5), pages 1-32, February.
    4. Roseline Oluwaseun Ogundokun & Sanjay Misra & Mychal Douglas & Robertas Damaševičius & Rytis Maskeliūnas, 2022. "Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks," Future Internet, MDPI, vol. 14(5), pages 1-20, May.

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