Advertisement

A Comparative Study of Local Classifiers Based on Clustering Techniques and One-Layer Neural Networks

  • Yuridia Gago-Pallares
  • Oscar Fontenla-Romero
  • Amparo Alonso-Betanzos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

In this article different approximations of a local classifier algorithm are described and compared. The classification algorithm is composed by two different steps. The first one consists on the clustering of the input data by means of three different techniques, specifically a k-means algorithm, a Growing Neural Gas (GNG) and a Self-Organizing Map (SOM). The groups of data obtained are the input to the second step of the classifier, that is composed of a set of one-layer neural networks which aim is to fit a local model for each cluster. The three different approaches used in the first step are compared regarding several parameters such as its dependence on the initial state, the number of nodes employed and its performance. In order to carry out the comparative study, two artificial and three real benchmark data sets were employed.

Keywords

Local Model Radial Basis Function Breast Cancer Dataset Nonlinear Activation Function Input Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, Reading, MA (1990)Google Scholar
  2. 2.
    Ridella, S., Rovetta, S., Zunino, R.: The k-winner machine model. In: IJCNN 2000. Int. Joint Conference on Neural Networks, vol. 1, pp. 106–111. IEEE, Los Alamitos (2000)Google Scholar
  3. 3.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)Google Scholar
  4. 4.
    Carpenter, G., Grossberg, S.: The art of adaptive pattern recognition by a self-organizing neural network. IEEE Computer 21(3), 77–88 (1988)Google Scholar
  5. 5.
    Kohonen, T.: Self-organizing maps. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  6. 6.
    Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Computer Systems 2, 321–355 (1988)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Bottou, L., Vapnik, V.: Local learning algorithms. Neural Computation 4, 888–900 (1992)CrossRefGoogle Scholar
  8. 8.
    Rumelhart, D.E., Hinton, G.E., Willian, R.J.: Learning representations of back-propagation errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  9. 9.
    Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993)CrossRefGoogle Scholar
  10. 10.
    Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)CrossRefGoogle Scholar
  11. 11.
    Castillo, E., Fontenla-Romero, O., Alonso-Betanzos, A., Guijarro-Berdiñas, B.: A global optimum approach for one-layer neural networks. Neural Computation 14(6), 1429–1449 (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    Newman, D., Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  13. 13.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  14. 14.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems 7 (NIPS 1994), pp. 625–632. MIT Press, Cambridge, MA (1995)Google Scholar
  15. 15.
    Rodríguez-Pena, R.M., Pérez-Sánchez, B., Fontenla-Romero, O.: A novel local classification method using growing neural gas and proximal support vector machines. In: IJCNN 2007. Int. Joint Conference on Neural Networks, IEEE, Los Alamitos (2007)Google Scholar
  16. 16.
    Computational Intelligence Laboratory, Department of Informatics, Nicolaus Copernicus University: Datasets used for classification: comparison of results (Last access July 17, 2007), http://www.fizyka.umk.pl/kmk/projects/datasets.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yuridia Gago-Pallares
    • 1
  • Oscar Fontenla-Romero
    • 1
  • Amparo Alonso-Betanzos
    • 1
  1. 1.University of A Coruña, Department of Computer Science, 15071 A CoruñaSpain

Personalised recommendations