Learning of neural networks with GA-based instance selection

摘要

We examine the effect of instance and feature selection on the generalization ability of trained neural networks for pattern classification problems. Before the learning of neural networks, a genetic-algorithm-based instance and feature selection method is applied for reducing the size of training data. Nearest neighbor classification is used for evaluating the classification ability of subsets of training data in instance and feature selection. Neural networks are trained by the selected subset (i.e., reduced training data). In this paper, we first explain our GA-based instance and feature selection method. Then we examine the effect of instance and feature selection on the generalization ability of trained neural networks through computer simulations on various artificial and real-world pattern classification problems.