Incremental Rule Pruning for Fuzzy ARTMAP Neural Network

  • A. Andrés-Andrés
  • E. Gómez-Sánchez
  • M. L. Bote-Lorenzo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)

Abstract

Fuzzy ARTMAP is capable of incrementally learning interpretable rules. To remove unused or inaccurate rules, a rule pruning method has been proposed in the literature. This paper addresses its limitations when incremental learning is used, and modifies it so that it does not need to store previously learnt samples. Experiments show a better performance, especially in concept drift problems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Networks 3, 698–713 (1992)CrossRefGoogle Scholar
  2. 2.
    Carpenter, G.A., Tan, H.A.: Rule extraction: From neural architecture to symbolic representation. Connection Science 7, 3–27 (1995)CrossRefGoogle Scholar
  3. 3.
    Gómez-Sánchez, E., Dimitriadis, Y.A., Cano-Izquierdo, J.M., López-Coronado, J.: μARTMAP: use of mutual information for category reduction in fuzzy ARTMAP. IEEE Trans. Neural Networks 13, 58–69 (2002)CrossRefGoogle Scholar
  4. 4.
    Schimmer, J., Granger, R.: Beyond incremental procesing: Tracking concept drift. In: Proc. Fifth Nat. Conf. Artificial Intelligence, Philadelphia, PA, pp. 502–507 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • A. Andrés-Andrés
    • 1
  • E. Gómez-Sánchez
    • 1
  • M. L. Bote-Lorenzo
    • 1
  1. 1.School of Telecommunications EngineeringUniversity of ValladolidValladolidSpain

Personalised recommendations