The Influence of Supervised Clustering for RBFNN Centers Definition: A Comparative Study

  • André R. Gonçalves
  • Rosana Veroneze
  • Salomão Madeiro
  • Carlos R. B. Azevedo
  • Fernando J. Von Zuben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)


Several clustering algorithms have been considered to determine the centers and dispersions of the hidden layer neurons of Radial Basis Function Neural Networks (RBFNNs) when applied both to regression and classification tasks. Most of the proposed approaches use unsupervised clustering techniques. However, for data classification, by performing supervised clustering it is expected that the obtained clusters represent meaningful aspects of the dataset. We therefore compared the original versions of k-means, Neural-Gas (NG) and Adaptive Radius Immune Algorithm (ARIA) along with their variants that use labeled information. The first two had already supervised versions in the literature, and we extended ARIA toward a supervised version. Artificial and real-world datasets were considered in our experiments and the results showed that supervised clustering is better indicated in problems with unbalanced and overlapping classes, and also when the number of input features is high.


Radial Basis Function Clustering Adaptive Radius Immune Algorithm Supervised Learning for Data Classification 


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • André R. Gonçalves
    • 1
  • Rosana Veroneze
    • 1
  • Salomão Madeiro
    • 1
  • Carlos R. B. Azevedo
    • 1
  • Fernando J. Von Zuben
    • 1
  1. 1.School of Electrical and Computer EngineeringUniversity of CampinasCampinasBrazil

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