Towards crafting a smooth and accurate functional link artificial neural networks based on differential evolution and feature selection for noisy database

This work presents an accurate and smooth functional link artificial neural network (FLANN) for classification of noisy database. The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of FLANN using differential evolution and filter based feature selection. We use Qclean algorithm for identification of noise, information gain theory for filtering irrelevant features, and then supplied the remaining relevant attributes to the functional expansion unit of FLANN, which in turn map lower to higher dimensional feature space for constructing a smooth and accurate classifier. In specific, the differential evolution is used to fine tune the weight vector of this network and some trigonometric functions are used in functional expansion unit. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository with a range of 5-20% noise. The insightful experimental study signifies the propensity of noise in the classification accuracy of a database with a range of noise from 5-20%. Moreover, our method suggests that noisy samples along with irrelevant set of attributes are deceptive and weakening the reliability of the classifier, therefore, it is required to reduce its effect before or during the process of classification.