IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v9y2018i3p75-87.html
   My bibliography  Save this article

Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

Author

Listed:
  • Noria Bidi

    (Department of Science and Technology, University Mustapha Stamboli, Mascara, Algeria)

  • Zakaria Elberrichi

    (Department of Computer Science, University Djillali Liabes, Sidi Bel Abbes, Algeria)

Abstract

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.

Suggested Citation

  • Noria Bidi & Zakaria Elberrichi, 2018. "Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(3), pages 75-87, July.
    • Handle: RePEc:igg:jamc00:v:9:y:2018:i:3:p:75-87
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAMC.2018070104
    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:9:y:2018:i:3:p:75-87. 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.