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An Efficient Heap Based Optimizer Algorithm for Feature Selection

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  • Mona A. S. Ali

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
    Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt)

  • Fathimathul Rajeena P. P.

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia)

  • Diaa Salama Abd Elminaam

    (Department of Computer Science, Faculty of Computers and Information, Misr International University, Cairo 12585, Egypt
    Department of Information System, Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt)

Abstract

The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B _ H B O are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B _ H B O are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B _ H B O is superior or equivalent to the other algorithms used in the literature.

Suggested Citation

  • Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "An Efficient Heap Based Optimizer Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(14), pages 1-33, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2396-:d:858487
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    References listed on IDEAS

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    1. Minh-Quang Tran & Yi-Chen Li & Chen-Yang Lan & Meng-Kun Liu, 2020. "Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region," Energies, MDPI, vol. 13(24), pages 1-16, December.
    2. Hossam M Zawbaa & E Emary & Crina Grosan, 2016. "Feature Selection via Chaotic Antlion Optimization," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-21, March.
    3. Xuyang Teng & Hongbin Dong & Xiurong Zhou, 2017. "Adaptive feature selection using v-shaped binary particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-22, March.
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    Cited by:

    1. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
    2. Walaa N. Ismail & Fathimathul Rajeena P. P. & Mona A. S. Ali, 2023. "A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    3. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.

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