Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison

Abstract

This paper presents an empirical comparison of three classification methods: neural networks, decision tree induction and linear discriminant analysis. The comparison is based on seven datasets with different characteristics, four being real, and three artificially created. Analysis of variance was used to detect any significant differences between the performance of the methods. There is also some discussion of the problems involved with using neural networks and, in particular, on overfitting of the training data. A comparison between two methods to prevent overfitting is presented: finding the most appropriate network size, and the use of an independent validation set to determine when to stop training the network.