Comparing generalised additive neural networks with decision trees and alternating conditional expectations

Abstract

In this dissertation generalised additive neural networks (GANNs), decision trees and alternating conditional expectations (ACE) are studied and compared as computerised techniques in the field of predictive data mining. De Waal and Du Toit (2003) developed the automated construction of GANNs called AutoGANN. The aim is to better understand the contribution that this new technology may bring to the field of data mining. Decision trees and ACE were chosen as comparative techniques since both have been praised for their performance as data analysis tools. The ACE, AutoGANN and decision tree modelling methods are explained and applied to several regression problems. The methods and empirical results are compared and discussed in terms of criteria such as predictive capability or accuracy, novelty, utility, interpretability and stability. Finally, the conclusion is made that the AutoGANN system should contribute substantially to the field of data mining.