Rule extraction from differential evolution trained radial basis function network using genetic algorithms

Authors:
Nekuri Naveen;V. Ravi;C. Raghavendra Rao
Affiliations:
Institute for Development and Research in Banking Technology, Hyderabad, AP, India and Department of Computer and Information Sciences, School of MCIS, University of Hyderabad, Hyderabad, AP, Indi ...;Institute for Development and Research in Banking Technology, Hyderabad, AP, India;Department of Computer and Information Sciences, School of MCIS, University of Hyderabad, Hyderabad, AP, India
Venue:
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Year:
2009

Citing 13
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Abstract

In this paper, we present a GA based methodology for extracting rules from radial basis function neural network trained by differential evolution. Rules are extracted using GATree. Here outputs predicted by the differential evolution trained radial basis function network along with the input variables are fed to the GATree for rule extraction purpose. The performance of the hybrid method was tested on three benchmark datasets namely Iris, Wine and Wisconsin Breast Cancer, using 10-fold cross validation. The rules extracted by the hybrid yielded high accuracies on all datasets.