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Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection

Author

Listed:
  • Hema Banati

    (Dyal Singh College)

  • Richa Sharma

    (Keshav Mahavidyalaya)

  • Asha Yadav

    (University of Delhi)

Abstract

Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA’s performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters.

Suggested Citation

  • Hema Banati & Richa Sharma & Asha Yadav, 2024. "Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 216-244, July.
    • Handle: RePEc:spr:jclass:v:41:y:2024:i:2:d:10.1007_s00357-024-09468-0
    DOI: 10.1007/s00357-024-09468-0
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    References listed on IDEAS

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    1. Mohammad H. Nadimi-Shahraki & Ali Fatahi & Hoda Zamani & Seyedali Mirjalili, 2022. "Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
    2. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.
    3. Hekmat Mohammadzadeh & Farhad Soleimanian Gharehchopogh, 2021. "Feature Selection with Binary Symbiotic Organisms Search Algorithm for Email Spam Detection," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 469-515, January.
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