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Hybrid Evolutionary Algorithm with Product-Unit Neural Networks for Classification

  • Francisco J. Martínez-Estudillo
  • César Hervás-Martínez
  • Alfonso C. Martínez-Estudillo
  • Pedro A. Gutiérrez-Peña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

In this paper we propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks, and on a dynamic version of a hybrid evolutionary neural network algorithm. The method combines an evolutionary algorithm, a clustering process, and a local search procedure, where the clustering process and the local search are only applied at specific stages of the evolutionary process. Our results with the product-unit models and the evolutionary approach show a very interesting performance in terms of classification accuracy, yielding a state-of-the-art performance.

Keywords

Classification Product-Unit Neural Networks Evolutionary algorithms 

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References

  1. 1.
    Durbin, R., Rumelhart, D.: Products Units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1, 133–142 (1989)CrossRefGoogle Scholar
  2. 2.
    Schmitt, M.: On the Complexity of Computing and Learning with Multiplicative Neural Networks. Neural Computation 14, 241–301 (2001)CrossRefGoogle Scholar
  3. 3.
    Martinez-Estudillo, A., et al.: Evolutionary product unit based neural networks for regression. Neural Networks 19(4), 477–486 (2006)zbMATHCrossRefGoogle Scholar
  4. 4.
    Ismail, A., Engelbrecht, A.P.: Global optimization algorithms for training product units neural networks. In: International Joint Conference on Neural Networks, IJCNN‘2000, Como, Italy (2000)Google Scholar
  5. 5.
    Janson, D.J., Frenzel, J.F.: Training product unit neural networks with genetic algorithms. IEEE Expert 8(5), 26–33 (1993)CrossRefGoogle Scholar
  6. 6.
    Engelbrecht, A.P., Ismail, A.: Training product unit neural networks. Stability and Control: Theory and Applications 2(1-2), 59–74 (1999)Google Scholar
  7. 7.
    Saito, K., Nakano, R.: Extracting Regression Rules From Neural Networks. Neural Networks 15, 1279–1288 (2002)CrossRefGoogle Scholar
  8. 8.
    Rechenberg, I.: Evolutionstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution. Framman-Holzboog, Stuttgart (1975)Google Scholar
  9. 9.
    Angeline, P.J., Saunders, G.M., Pollack, J.B.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks 5(1), 54–65 (1994)CrossRefGoogle Scholar
  10. 10.
    Blake, C., Merz, C.J.: UCI repository of machine learning data bases (1998), http://www.ics.uci.edu/mlearn/MLRepository.thml
  11. 11.
    Landwehr, N., Hall, M., Eibe, F.: Logistic Model Trees. Machine Learning 59, 161–205 (2005)zbMATHCrossRefGoogle Scholar
  12. 12.
    Breiman, L., et al.: Classification and Regression Trees. Wadsworth, Belmont (1984)zbMATHGoogle Scholar
  13. 13.
    Kohavi, R.: Scaling up the accuracy of naive bayes classifiers: A decision-tree hybrid. In: Proc. 2nd International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park (1996)Google Scholar
  14. 14.
    Gama, J.: Functional trees. Machine Learning 55(3), 219–250 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    Wang, Y., Witten, I.: Inducing model trees for continuous classes. In: Proceedings of Poster Papers, European Conference on Machine Learning, Prague, Czech Republic (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Francisco J. Martínez-Estudillo
    • 1
  • César Hervás-Martínez
    • 2
  • Alfonso C. Martínez-Estudillo
    • 1
  • Pedro A. Gutiérrez-Peña
    • 2
  1. 1.Department of Management and Quantitative Methods, ETEASpain
  2. 2.Department of Computing and Numerical Analysis of the University of CórdobaSpain

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