ROC analysis as a useful tool for performance evaluation of artificial neural networks

Abstract

In many applications of neural networks, the performance of the network is given by the classification accuracy. While obtaining the classification accuracies, the total true classification is computed, but the number of classification rates of the classes and fault classification rates are not given. This would not be enough for a problem having fatal importance. As an implementation example, a dataset having fatal importance is classified by MLP, RBF, GRNN, PNN and LVQ networks and the real performances of these networks are found by applying ROC analysis.