Welcome to the InfoSci Platform
Reference Hub5
Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification

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

Sanjeev Kumar Dash, Aditya Prakash Dash, Satchidananda Dehuri, Sung-Bae Cho
Copyright: © 2013 |Volume: 4 |Issue: 1 |Pages: 18
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466631182|DOI: 10.4018/jamc.2013010103
Cite Article Cite Article

MLA

Dash, Sanjeev Kumar, et al. "Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification." IJAMC vol.4, no.1 2013: pp.32-49. http://doi.org/10.4018/jamc.2013010103

APA

Dash, S. K., Dash, A. P., Dehuri, S., & Cho, S. (2013). Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification. International Journal of Applied Metaheuristic Computing (IJAMC), 4(1), 32-49. http://doi.org/10.4018/jamc.2013010103

Chicago

Dash, Sanjeev Kumar, et al. "Feature Selection for Designing a Novel Differential Evolution Trained Radial Basis Function Network for Classification," International Journal of Applied Metaheuristic Computing (IJAMC) 4, no.1: 32-49. http://doi.org/10.4018/jamc.2013010103

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.