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Title: Cross-validation in Fuzzy ARTMAP for large databases
Authors: Koufakou, Anna
Georgiopoulos, Michael N.
Kasparis, Takis 
Keywords: Accuracy;Neural networks;Fuzzy logic;Computer software--Validation
Issue Date: 2001
Publisher: Elsevier
Source: Neural Networks, 2001, Volume 14, Issue 9, Pages 1279–1291
Abstract: In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy 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 Fuzzy ARTMAP neural network architectures. In this work, we are demonstrating that overtraining happens in Fuzzy ARTMAP and we propose an old remedy for its cure: cross-validation. In our experiments, we compare the performance of Fuzzy 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 artificial and real databases. The conclusion derived from those experiments is that cross-validation is a useful procedure in Fuzzy ARTMAP, because it produces smaller Fuzzy ARTMAP architectures with improved generalization performance. The trade-off is that cross-validation introduces additional computational complexity in the training phase of Fuzzy ARTMAP.
ISSN: 08936080
Rights: © 2001 Elsevier Science Ltd
Type: Article
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