IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v9y2024i2p20-d1326020.html
   My bibliography  Save this article

An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Author

Listed:
  • Amani Abdo

    (Faculty of Computing, Arab Open University, El-Shorouk 51, Cairo 11211, Egypt
    Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

  • Rasha Mostafa

    (Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

  • Laila Abdel-Hamid

    (Faculty of Computers & Artificial Intelligence, Helwan University (HU), Ain Helwan, Cairo 11795, Egypt)

Abstract

Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches.

Suggested Citation

  • Amani Abdo & Rasha Mostafa & Laila Abdel-Hamid, 2024. "An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms," Data, MDPI, vol. 9(2), pages 1-17, January.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:2:p:20-:d:1326020
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/9/2/20/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/9/2/20/
    Download Restriction: no
    ---><---

    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:jdataj:v:9:y:2024:i:2:p:20-:d:1326020. 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: 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.