Title
Cross-validation in Fuzzy ARTMAP for large databases
Abbreviated Journal Title
Neural Netw.
Keywords
Fuzzy ARTMAP; cross-validation; overtraining; generalization performance; NEURAL-NETWORK; STATISTICAL-THEORY; CLASSIFICATION; ARCHITECTURE; Computer Science, Artificial Intelligence
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. (C) 2001 Elsevier Science Ltd. All rights reserved.
Journal Title
Neural Networks
Volume
14
Issue/Number
9
Publication Date
1-1-2001
Document Type
Article
Language
English
First Page
1279
Last Page
1291
WOS Identifier
ISSN
0893-6080
Recommended Citation
"Cross-validation in Fuzzy ARTMAP for large databases" (2001). Faculty Bibliography 2000s. 8074.
https://stars.library.ucf.edu/facultybib2000/8074
Comments
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