IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v22y2023i01ns0219622022500432.html
   My bibliography  Save this article

BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data

Author

Listed:
  • Aitak Shaddeli

    (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran)

  • Farhad Soleimanian Gharehchopogh

    (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran)

  • Mohammad Masdari

    (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran)

  • Vahid Solouk

    (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran†Faculty of Information Technology and Computer Engineering, Urmia University of Technology, Urmia, Iran)

Abstract

Feature selection is one of the main issues in machine learning algorithms. In this paper, a new binary hyper-heuristics feature ranks algorithm is designed to solve the feature selection problem in high-dimensional classification data called the BFRA algorithm. The initial strong population generation is done by ranking the features based on the initial Laplacian Score (ILR) method. A new operator called AHWF removes the zero-importance or redundant features from the population-based solutions. Another new operator, AHBF, selects the key features in population-based solutions. These two operators are designed to increase the exploitation of the BFRA algorithm. To ensure exploration, we introduced a new operator called BOM, a binary counter-mutation that increases the exploration and escape from the BFRA algorithm’s local trap. Finally, the BFRA algorithm was evaluated on 26 high-dimensional data with different statistical criteria. The BFRA algorithm has been tested with various meta-heuristic algorithms. The experiments’ different dimensions show that the BFRA algorithm works like a robust meta-heuristic algorithm in low dimensions. Nevertheless, by increasing the dataset dimensions, the BFRA performs better than other algorithms in terms of the best fitness function value, accuracy of the classifiers, and the number of selected features compared to different algorithms. However, a case study of sentiment analysis of movie viewers using BFRA proves that BFRA algorithms demonstrate affordable performance.

Suggested Citation

  • Aitak Shaddeli & Farhad Soleimanian Gharehchopogh & Mohammad Masdari & Vahid Solouk, 2023. "BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 471-536, January.
  • Handle: RePEc:wsi:ijitdm:v:22:y:2023:i:01:n:s0219622022500432
    DOI: 10.1142/S0219622022500432
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622022500432
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219622022500432?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:wsi:ijitdm:v:22:y:2023:i:01:n:s0219622022500432. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    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.