IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i14p2396-d858487.html
   My bibliography  Save this article

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/14/2396/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/14/2396/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    2. Bingtao Zhang & Peng Cao, 2019. "Classification of high dimensional biomedical data based on feature selection using redundant removal," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
    3. Muhammad Huzaifa & Arif Hussain & Waseem Haider & Syed Ali Abbas Kazmi & Usman Ahmad & Habib Ur Rehman, 2023. "Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation," Sustainability, MDPI, vol. 15(9), pages 1-32, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2396-:d:858487. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.