Cross-validation in Fuzzy ARTMAP for large databases

    Authors

    A. Koufakou; M. Georgiopoulos; G. Anagnostopoulos;T. Kasparis

    Comments

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    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

    WOS:000171910800012

    ISSN

    0893-6080

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