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Hypothesis Diversity in Ensemble Classification

  • Lorenza Saitta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

The paper discusses the issue of hypothesis diversity in ensemble classifiers. The measures of diversity previously proposed in the literature are analyzed inside a unifying framework based on Monte Carlo stochastic algorithms. The paper shows that no measure is useful to predict ensemble performance, because all of them have only a very loose relation with the expected accuracy of the classifier.

Keywords

Diversity Measure Unify Framework Entropy Measure Machine Learning Research Neural Network Ensemble 
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.

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References

  1. 1.
    Dietterich, T.: Machine learning research: Four current directions (1997)Google Scholar
  2. 2.
    Esposito, R., Saitta, L.: A monte carlo analysis of ensemble classification. In: Greiner, R., Schuurmans, D. (eds.) Proceedings of the twenty-first International Conference on Machine Learning, Banff, Canada, July 2004, pp. 265–272. ACM Press, New York (2004)Google Scholar
  3. 3.
    Esposito, R., Saitta, L.: Experimental Comparison between Bagging and Monte Carlo Ensemble Classification. In: Raedt, L.D., Wrobel, S. (eds.) Proceedings of the twenty-second International Conference of Machine Learning, Bonn, Germany, August 2005, ACM Press, New York (2005)Google Scholar
  4. 4.
    Hansen, L.K., Salamon, P.: Neural networks ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, L.: That elusive diversity in classifier ensembles (2003)Google Scholar
  6. 6.
    Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51, 181–207 (2003)MATHCrossRefGoogle Scholar
  7. 7.
    Partridge, D., Krzanowski, W.: Distinct failure diversity in multiversion software (1997)Google Scholar
  8. 8.
    Rao, C.R.: Diversity: Its measurement, decomposition, apportionment and analysis. The Indian Journal of Statistics, Series A 44, 1–22 (1982)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lorenza Saitta
    • 1
  1. 1.Dipartimento di InformaticaUniversità del Piemonte OrientaleAlessandriaItaly

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