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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Durbin, R., Rumelhart, D.: Products Units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1, 133–142 (1989)
Schmitt, M.: On the Complexity of Computing and Learning with Multiplicative Neural Networks. Neural Computation 14, 241–301 (2001)
Martinez-Estudillo, A., et al.: Evolutionary product unit based neural networks for regression. Neural Networks 19(4), 477–486 (2006)
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)
Janson, D.J., Frenzel, J.F.: Training product unit neural networks with genetic algorithms. IEEE Expert 8(5), 26–33 (1993)
Engelbrecht, A.P., Ismail, A.: Training product unit neural networks. Stability and Control: Theory and Applications 2(1-2), 59–74 (1999)
Saito, K., Nakano, R.: Extracting Regression Rules From Neural Networks. Neural Networks 15, 1279–1288 (2002)
Rechenberg, I.: Evolutionstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution. Framman-Holzboog, Stuttgart (1975)
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)
Blake, C., Merz, C.J.: UCI repository of machine learning data bases (1998), http://www.ics.uci.edu/mlearn/MLRepository.thml
Landwehr, N., Hall, M., Eibe, F.: Logistic Model Trees. Machine Learning 59, 161–205 (2005)
Breiman, L., et al.: Classification and Regression Trees. Wadsworth, Belmont (1984)
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)
Gama, J.: Functional trees. Machine Learning 55(3), 219–250 (2004)
Wang, Y., Witten, I.: Inducing model trees for continuous classes. In: Proceedings of Poster Papers, European Conference on Machine Learning, Prague, Czech Republic (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Estudillo, F.J., Hervás-Martínez, C., Martínez-Estudillo, A.C., Gutiérrez-Peña, P.A. (2007). Hybrid Evolutionary Algorithm with Product-Unit Neural Networks for Classification. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)