DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

This chapter presents a novel approach for classification of 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 its size. In this chapter, 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. Different feature selection methods, handling missing values and removal of inconsistency to improve the classification accuracy of the proposed model are emphasized. 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.