DE+RBFNs based classification: A special attention to removal of inconsistency and irrelevant features
A novel approach for the classification of both balanced and imbalanced dataset is developed in this paper by integrating the best attributes of radial basis function networks and differential evolution. In addition, a special attention is given to handle the problem of inconsistency and removal of irrelevant features. Removing data inconsistency and inputting optimal and relevant set of features to a radial basis function network may greatly enhance the network efficiency (in terms of accuracy), at the same time compact its size. We use Bayesian statistics for making the dataset consistent, information gain theory (a kind of filter approach) for reducing the features, and differential evolution for tuning center, spread and bias of radial basis function networks. The proposed approach is validated with a few benchmarked highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. Our experimental result demonstrates promising classification accuracy, when data inconsistency and feature selection are considered to design this classifier.
classification differential evolution dataset consistency feature selection radial basis function neural networks