Factors of overtraining with fuzzy ARTMAP neural networks

In this paper, the impact of overtraining on the performance of fuzzy ARTMAP neural networks is assessed for pattern recognition problems consisting of overlapping class distributions, and consisting of complex decision boundaries with no overlap. Computer simulations are performed with fuzzy ARTMAP networks trained for one epoch, through cross-validation, and until network convergence, using several data sets representing these pattern recognition problems. By comparing the generalisation error and resources required by these networks, the extent of overtraining due to factors such as data set structure, training strategy, number of training epochs, data normalisation, and training set size, is demonstrated. A significant degradation in fuzzy ARTMAP performance due to overtraining is shown to depend on the training set size and the number of training epochs for pattern recognition problems with overlapping class distributions.