IDEAS home Printed from https://ideas.repec.org/a/arp/srarsr/2015p74-78.html
   My bibliography  Save this article

Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Network

Author

Listed:
  • Lim Eng Aik

    (Institute of Engineering Mathematic Universiti Malaysia Perlis, 02600 Ulu Pauh, Perlis)

  • Mohd. Syafarudy Abu

    (Institute of Engineering Mathematic Universiti Malaysia Perlis, 02600 Ulu Pauh, Perlis)

Abstract

Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.

Suggested Citation

  • Lim Eng Aik & Mohd. Syafarudy Abu, 2015. "Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Network," Scientific Review, Academic Research Publishing Group, vol. 1(4), pages 74-78, 09-2015.
    • Handle: RePEc:arp:srarsr:2015:p:74-78
    DOI: arpgweb.com/?ic=journal&journal=10&info=aims
    as

    Download full text from publisher

    File URL: http://www.arpgweb.com/pdf-files/sr1(4)74-78.pdf
    Download Restriction: no
    File URL: http://www.arpgweb.com/?ic=journal&journal=10&month=09-2015&issue=4&volume=1
    Download Restriction: no
    File URL: https://libkey.io/arpgweb.com/?ic=journal&journal=10&info=aims?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
    ---><---

    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:arp:srarsr:2015:p:74-78. 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: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/index.php?ic=journal&journal=10&info=aims .

    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.