IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v27y2024i10p1224-1238.html
   My bibliography  Save this article

SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19

Author

Listed:
  • Robinson Joel M
  • Manikandan G
  • Bhuvaneswari G
  • Shanthakumar P

Abstract

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..

Suggested Citation

  • Robinson Joel M & Manikandan G & Bhuvaneswari G & Shanthakumar P, 2024. "SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(10), pages 1224-1238, July.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:10:p:1224-1238
    DOI: 10.1080/10255842.2023.2236744
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2023.2236744
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10255842.2023.2236744?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.

    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:taf:gcmbxx:v:27:y:2024:i:10:p:1224-1238. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    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.