Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7128
Title: Cross-validation in fuzzy ARTMAP neural networks for large sample classification problems
Authors: Kasparis, Takis 
Georgiopoulos, Michael N.
Koufakou, Anna
Keywords: Neural networks
Classification
Databases
Issue Date: 2001
Publisher: SPIE
Source: Applications and Science of Computational Intelligence IV, 2001, Orlando, Florida
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: crossvalidation. 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 these 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.
URI: http://ktisis.cut.ac.cy/handle/10488/7128
ISSN: 0277786X
DOI: 10.1117/12.421155
Rights: © 2001 SPIE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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