Title

Cross-Validation In Fuzzy Artmap Neural Networks For Large Sample Classification Problems

Keywords

Cross-validation; Fuzzy ARTMAP; Generalization performance; Overtraining

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.

Publication Date

1-1-2001

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

4390

Number of Pages

1-11

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.421155

Socpus ID

0034939898 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0034939898

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