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

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.