Advertisement

Ensemble Methods for Boosting Visualization Models

  • Bruno Baruque
  • Emilio Corchado
  • Aitor Mata
  • Juan M. Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)

Abstract

Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a study of the combination of different ensemble training techniques with a novel summarization algorithm for ensembles of topology preserving models. The aim of these techniques is the increase of the truthfulness of the visualization of the dataset obtained by this kind of algorithms and, as an extension, the stability conditions of the former. A study and comparison of the performance of some novel and classical ensemble techniques, using well-known datasets from the UCI repository (Iris and Wine), are presented in this paper to test their suitability, in the fields of data visualization and topology preservation when combined with one of the most widespread of that kind of models such as the Self-Organizing Map.

Keywords

topology preserving mappings boosting bagging unsupervised learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Self-organizing maps. Series in Information Sciences, vol. 30. Springer, Berlin (1995)MATHGoogle Scholar
  2. 2.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)MATHGoogle Scholar
  3. 3.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Kohonen, T.: The Self-Organizing Map. Neurocomputing 21, 1–6 (1998)CrossRefMATHGoogle Scholar
  5. 5.
    Kohonen, T., Lehtio, P., Rovamo, J., et al.: A Principle of Neural Associative Memory. Neuroscience 2, 1065–1076 (1977)CrossRefGoogle Scholar
  6. 6.
    Pölzlbauer, G.: Survey and Comparison of Quality Measures for Self-Organizing Maps. In: WDA 2004, pp. 67–82 (2004)Google Scholar
  7. 7.
    Polani, D.: Measures for the organization of self-organizing maps. In: Seiffert, U., Jain, L.C. (eds.) Self-Organizing Neural Networks: Recent Advances and Applications. Studies in Fuzziness and Soft Computing, vol. 16, pp. 13–44. Physica-Verlag, Heidelberg (2003)Google Scholar
  8. 8.
    Vesanto, J.: Data Mining Techniques Based on the Self-Organizing Map, 63 (1997)Google Scholar
  9. 9.
    Kiviluoto, K.: Topology Preservation in Self-Organizing Maps. In: ICNN 1996, vol. 1, pp. 294–299 (1996)Google Scholar
  10. 10.
    Lampinen, J.: On Clustering Properties of Hierarchical Self-Organizing Maps. Artificial Neural Networks 2, II, 1219–1222 (1992)CrossRefMATHGoogle Scholar
  11. 11.
    Vesanto, J., Sulkava, M., Hollmén, J.: On the Decomposition of the Self-Organizing Map Distortion Measure. In: WSOM 2003, pp. 11–16 (2003)Google Scholar
  12. 12.
    Kaski, S., Lagus, K.: Comparing Self-Organizing Maps. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, pp. 809–814. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  13. 13.
    Heskes, T.: Balancing between Bagging and Bumping. NIPS 9, 466–472 (1997)Google Scholar
  14. 14.
    Schwenk, H., Bengio, Y.: Boosting Neural Networks. Neural Computation 12, 1869–1887 (2000)CrossRefGoogle Scholar
  15. 15.
    Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm, pp. 148–156 (1996)Google Scholar
  16. 16.
    Baruque, B., Corchado, E., Yin, H.: Quality of Adaptation of Fusion ViSOM. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 728–738. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Corchado, E., Baruque, B., Yin, H.: Boosting Unsupervised Competitive Learning Ensembles. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 339–348. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Baruque, B., Corchado, E., Rovira, J., et al.: Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry, pp. 491–497 (2008)Google Scholar
  19. 19.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007) (2008)Google Scholar
  20. 20.
    Georgakis, A., Li, H., Gordan, M.: An Ensemble of SOM Networks for Document Organization and Retrieval. In: AKRR 2005, pp. 6–141 (2005)Google Scholar
  21. 21.
    Saavedra, C., Salas, R., Moreno, S., et al.: Fusion of Self Organizing Maps. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 227–234. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bruno Baruque
    • 1
  • Emilio Corchado
    • 1
  • Aitor Mata
    • 2
  • Juan M. Corchado
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosSpain
  2. 2.Department of Computer Science and AutomaticsUniversity of SalamancaSpain

Personalised recommendations