IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v12y2021i2p85-99.html
   My bibliography  Save this article

A Novel Dynamic Hybridization Method for Best Feature Selection

Author

Listed:
  • Nassima Dif

    (EEDIS Laboratory, Djillali Liabes University, Algeria)

  • Zakaria Elberrichi

    (EEDIS Laboratory, Djillali Liabes University, Algeria)

Abstract

Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.

Suggested Citation

  • Nassima Dif & Zakaria Elberrichi, 2021. "A Novel Dynamic Hybridization Method for Best Feature Selection," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 12(2), pages 85-99, April.
  • Handle: RePEc:igg:jamc00:v:12:y:2021:i:2:p:85-99
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAMC.2021040106
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jamc00:v:12:y:2021:i:2:p:85-99. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.