Hypothesis Diversity in Ensemble Classification

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


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.


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