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A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques

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Abstract

Feature selection plays a pivotal role in machine learning, serving as a critical preprocessing step. Its impact extends beyond enhancing the classification capabilities of learning algorithms; it also enables the reduction of dataset dimensionality. Consequently, feature selection entails a multi-objective optimization problem, striving to minimize the number of features while maximizing classification accuracy. Surprisingly, there are only a few studies that approach feature selection from a multi-objective perspective, compared to the more prevalent single-objective viewpoint.Motivated by this gap, we present a novel multi-objective algorithm for tackling the feature selection problem. Our approach draws inspiration from quantum computing and combines the strengths of the Firefly Algorithm (FA) and the Particle Swarm Optimizer (PSO). Leveraging quantum computing enhances solution distribution, while the cooperative nature of FA and PSO facilitates effective exploration of the feature space. Additionally, we introduce two fixed-size external archives, dedicated to storing the best solutions. The archive sizes are controlled using the epsilon dominance relation. We evaluate the efficiency of our algorithm through an extensive comparison against both single and multi-objective feature selection algorithms that enjoy high regard in the field. Furthermore, we propose a high-performance detection system that harnesses our algorithm alongside three Convolutional Neural Network Algorithms. This system demonstrates its potential in accurately identifying COVID-19 disease from X-ray images. Our experimental results unequivocally establish the superiority of our proposed algorithm over its competitors. It consistently delivers feature subsets with a smaller number of features and achieves higher classification accuracy.

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Authors and Affiliations

  1. Department of Computer Science, University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria

    Djaafar Zouache

  2. LRIA Laboratory, University of Science and Technology Houari Boumediene, Algiers, Algeria

    Djaafar Zouache & Adel Got

  3. College of Computing and Informatics, Saudi Electronic University, Abha, Saudi Arabia

    Deemah Alarabiat

  4. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  7. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  8. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  9. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  10. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia

    Laith Abualigah

  11. Polytech’Lille, University Lille - INRIA CRISTAL - CNRS, Lille, France

    El-Ghazali Talbi

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  1. Djaafar Zouache
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  2. Adel Got
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Correspondence to Djaafar Zouache.

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Zouache, D., Got, A., Alarabiat, D. et al. A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques. Multimed Tools Appl 83, 22811–22835 (2024). https://doi.org/10.1007/s11042-023-16411-9

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  • DOI: https://doi.org/10.1007/s11042-023-16411-9

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