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Related Experiment Videos

An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance.

I Dagher1, M Georgiopoulos, G L Heileman

  • 1Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
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A new ordering algorithm improves fuzzy adaptive resonance theory mapping (ARTMAP) generalization performance. This method, combined with ARTMAP, offers better or equal results than standard ARTMAP with fewer computational operations.

Area of Science:

  • Computational intelligence
  • Machine learning
  • Pattern recognition

Background:

  • Fuzzy adaptive resonance theory mapping (ARTMAP) is a supervised learning method for pattern recognition.
  • The order of training data presentation can significantly impact ARTMAP's generalization performance.
  • Existing ARTMAP methods may not consistently achieve optimal generalization due to arbitrary training orders.

Purpose of the Study:

  • To introduce a novel ordering algorithm for fuzzy ARTMAP to enhance generalization performance.
  • To analyze the computational complexity of the proposed ordering algorithm.
  • To compare the performance and efficiency of the ordered fuzzy ARTMAP against the standard fuzzy ARTMAP.

Main Methods:

  • The study introduces an ordering algorithm based on the max-min clustering method.

Related Experiment Videos

  • This algorithm determines a fixed, optimized order for presenting training patterns to fuzzy ARTMAP.
  • The combined approach is termed 'ordered fuzzy ARTMAP'.

    • Main Results:

    • Ordered fuzzy ARTMAP demonstrates superior generalization performance compared to the average performance of standard fuzzy ARTMAP.
    • In specific cases, ordered fuzzy ARTMAP achieves generalization performance comparable to or exceeding the best results from standard fuzzy ARTMAP.
    • The ordering algorithm requires a fraction of the computational operations of the fuzzy ARTMAP training phase.

    Conclusions:

    • The proposed ordering algorithm effectively enhances fuzzy ARTMAP generalization.
    • Ordered fuzzy ARTMAP offers a computationally efficient improvement over standard fuzzy ARTMAP.
    • This method provides a robust strategy for optimizing ARTMAP training pattern presentation.