Title

Cross-Validation In Fuzzy Artmap For Large Databases

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: 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. Copyright © 2001 Elsevier Science Ltd.

Publication Date

10-10-2001

Publication Title

Neural Networks

Volume

14

Issue

9

Number of Pages

1279-1291

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/S0893-6080(01)00090-9

Socpus ID

0034805236 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0034805236

This document is currently not available here.

Share

COinS