Cross-validation in fuzzy ARTMAP neural networks for large sample classification problems

Michael Georgiopoulos; Anna Koufakou; Georgios C. Anagnostopoulos; Takis Kasparis

doi:10.1117/12.421155

21 March 2001 Cross-validation in fuzzy ARTMAP neural networks for large sample classification problems

Michael Georgiopoulos, Anna Koufakou, Georgios C. Anagnostopoulos, Takis Kasparis

Proceedings Volume 4390, Applications and Science of Computational Intelligence IV; (2001) https://doi.org/10.1117/12.421155
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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 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.

Citation Download Citation

Michael Georgiopoulos, Anna Koufakou, Georgios C. Anagnostopoulos, and Takis Kasparis "Cross-validation in fuzzy ARTMAP neural networks for large sample classification problems", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); https://doi.org/10.1117/12.421155

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KEYWORDS

Fuzzy logic

Databases

Neural networks

Chromium

Error control coding

Machine learning

Statistical modeling

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