Wrapper Approach for Feature Selections in RBF Network Classifier

Abstract

In this paper we investigate the impact of wrapper approach on classification accuracy and performance of RBF network.
Wrapper approach used six rule induction algorithms for evaluators on supervised learning algorithms RBF network and tested
using eight real and three artificial benchmark data sets. Classification accuracy and performance of RBF network depends on
evaluators. Our experimental results indicate that every rule induction algorithms in wrapper approach maintains or improves the
accuracy of RBF network for more than half data sets. Evaluation of selecting features with wrappers approach is not so fast
compare with filters approach.