IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v4y2013i1p32-49.html
   My bibliography  Save this article

Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification

Author

Listed:
  • Sanjeev Kumar Dash

    (Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha, India)

  • Aditya Prakash Dash

    (Silicon Institute of Technology, Bhubaneswar, Odisha, India)

  • Satchidananda Dehuri

    (Department of Systems Engineering, Ajou University, Suwon, Korea)

  • Sung-Bae Cho

    (Department of Computer Science, Yonsei University, Seoul, Korea)

Abstract

This work presents a novel approach for classification of both balanced and unbalanced dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact it size. In this paper, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

Suggested Citation

  • Sanjeev Kumar Dash & Aditya Prakash Dash & Satchidananda Dehuri & Sung-Bae Cho, 2013. "Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 4(1), pages 32-49, January.
    • Handle: RePEc:igg:jamc00:v:4:y:2013:i:1:p:32-49
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jamc.2013010103
    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:4:y:2013:i:1:p:32-49. 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.