Overtraining in Fuzzy ARTMAP: Myth or Reality?

In this paper we are examining the issue of overtraining in Fuuy ARTMAP. Over-training in Fuuy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses, and (b) it creates unnecessarily large Fuuy ARTMAP neural network architectures. In this work we are demonstrating that overtraining happens in Fuuy ARTMAP and we propose an old remedy for its cure: crossvalidation. In our experiments we compare the performance of Fuuy ARTMAP that is trained (i) until the completion of training, (ii) for one epoch, and (iii) until Its performance on a validation set is maximized. The experiments were performed on artijcial and real databases. The conclusion derived from these experiments is that cross-validation is a useful procedure in Fuuy ARTMAP, because it produces smaller Fuuy ARTMAP architectures with improved generalization performance. The trade-off is that crossvalidation introduces additional computational complexity in the training phase of Fuuy ARTMAP.