Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification

Abstract

This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.