Designing RBFNNs Using Prototype Selection

  • Ana Cecilia Tenorio-González
  • José Fco. Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


Performance and accuracy of a neural network are strongly related to its design. Designing a neural network involves topology (number of neurons, number of layers, number of synapses between layers, etc.), training synapse weights, and parameter selection. Radial basis function neural networks (RBFNNs) could additionally require some other parameters, for example, the means and standard deviations if the activation function of neurons in the hidden layer is a Gaussian function. Commonly, Genetic Algorithms and Evolution Strategies have been used for automatically designing RBFNNs In this work, the use of prototype selection methods for designing a RBFNN is proposed and studied. Experimental results show the viability of designing RBFNNs using prototype selection.


Neural networks RBFNN prototype selection supervised classification 


  1. 1.
    Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  2. 2.
    Tian, J., Li, M., Chen, F.: A Cooperative Coevolution Algorithm of RBFNN for Classification. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 809–816. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Qin, Z., Chen, J., Liu, Y., Lu, J.: Evolving RBF Neural Networks for Pattern Classification. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 957–964. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Yang, B., Zhou, J.: Automatic Design of Hierarchical RBF Networks for System Identification. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 1191–1195. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Tian, J., Li, M., Chen, F.: Improving multiclass pattern recognition with a co-evolutionary RBFNN. Pattern Recognition Letters 29(4), 392–406 (2008)CrossRefGoogle Scholar
  6. 6.
    Tian, J., Li, M., Chen, F.: A hybrid classification algorithm based on co-evolutionary EBFNN and domain covering method. Neural Computing & Applications 18(3), 293–308 (2009)CrossRefGoogle Scholar
  7. 7.
    Parras-Gutierrez, E., Rivas, V.M., Del Jesus, M.J.: Automatic Neural Net Design by Means of a Symbiotic Co-evolutionary Algorithm. In: Corchado, E., Abraham, A., Pedrycz, W. (eds.) HAIS 2008. LNCS (LNAI), vol. 5271, pp. 140–147. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Mu, S., Tian, S., Yin, C.: A Novel Radial Basis Function Neural Network Classifier with Centers Set By Cooperative Clustering. International Journal of Fuzzy Systems 9(4), 205–211 (2007)MathSciNetGoogle Scholar
  9. 9.
    Pedrycz, W., Park, H.S., Oh, S.K.: A granular-oriented development of functional radial basis function neural networks. Neurocomputing 72(1-3), 420–435 (2008)CrossRefGoogle Scholar
  10. 10.
    Olvera-Lopez, A., Carrasco-Ochoa, J.A., Martinez-Trinidad, J.F.: Object Selection Based on Clustering and Border Objects. Computer Recognition Systems 2, Advances in Soft Computing 45, 27–34 (2007)CrossRefGoogle Scholar
  11. 11.
    Lumini, A., Nanni, L.: A clustering method for automatic biometric template selection. Pattern Recognition 39(3), 495–497 (2006)CrossRefzbMATHGoogle Scholar
  12. 12.
    Wilson, D.R., Martinez, T.R.: Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning 38(3), 257–286 (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Looney, C.G.: Pattern Recognition Using Neural Networks. In: Theory and Algorithms for Engineers and Scientists. Oxford University Press, Oxford (1997)Google Scholar
  14. 14.
    Rivas, V.M., Merelo, J.J., Castillo, P.A., Arenas, M.G., Castellano, J.G.: Evolving RBF neural networks for time-series forecasting with EvRBF. Information Sciences 165(3-4), 207–220 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA (1998),
  16. 16.
    Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F.: KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing 13(3), 307–318 (2009)CrossRefGoogle Scholar
  17. 17.
    Haykin, S.: Neural Networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs (2005)zbMATHGoogle Scholar
  18. 18.
    Konar, A.: Computational Intelligence: principles, techniques, and applications. Springer, Heidelberg (2005)CrossRefzbMATHGoogle Scholar
  19. 19.
    The MathWorks Inc., Natick (1994-2008),
  20. 20.
    Meffert, Klaus, et al.: JGAP - Java Genetic Algorithms and Genetic Programming Package,

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ana Cecilia Tenorio-González
    • 1
  • José Fco. Martínez-Trinidad
    • 1
  • Jesús Ariel Carrasco-Ochoa
    • 1
  1. 1.Optics and ElectronicsNational Institute for AstrophysicsPueblaMéxico

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