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Cross-validation in fuzzy ARTMAP for large databases.

A Koufakou1, M Georgiopoulos, G Anagnostopoulos

  • 1School of Electrical Engineering and Computer Science, University of Central Florida, Orlando 32816, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|November 23, 2001
PubMed
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Overtraining in Fuzzy ARTMAP degrades generalization and increases network size. Cross-validation effectively cures overtraining, yielding smaller Fuzzy ARTMAP networks with better performance.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Overtraining is a significant issue in Fuzzy ARTMAP, negatively impacting generalization performance.
  • Overtraining also leads to the development of unnecessarily large Fuzzy ARTMAP neural network architectures.

Purpose of the Study:

  • This study investigates the phenomenon of overtraining in Fuzzy ARTMAP.
  • The research proposes cross-validation as a method to mitigate overtraining issues.

Main Methods:

  • Experiments were conducted comparing Fuzzy ARTMAP training until completion, for one epoch, and until validation set performance is maximized.
  • The effectiveness of cross-validation was evaluated on both artificial and real-world databases.

Main Results:

Related Experiment Videos

  • Cross-validation was demonstrated to be a beneficial technique for Fuzzy ARTMAP.
  • Utilizing cross-validation resulted in smaller Fuzzy ARTMAP architectures.
  • The generalization performance of Fuzzy ARTMAP models was improved through cross-validation.

Conclusions:

  • Cross-validation is a valuable procedure for improving Fuzzy ARTMAP models.
  • The primary trade-off of using cross-validation is an increase in computational complexity during the training phase.