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

Sanjeev Kumar Dash, Aditya Prakash Dash, Satchidananda Dehuri and Sung-Bae Cho
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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

International Journal of Applied Metaheuristic Computing (IJAMC), 2013, vol. 4, issue 1, 32-49

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.

Date: 2013
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