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Information Theory and Classification Error in Probabilistic Classifiers

  • Aritz Páerez
  • Pedro Larrañaga
  • Iñaki Inza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)

Abstract

This work shows, using bivariate continuous artificial domains, the relation that seems to exist between some measures based on the information theory and the expected classification error.

The relations that seem to be found in this work could be applied to the improvement of the classifiers which assign a posteriori probabilities to each class value. They also could be used in other tasks related to the supervised classification such as feature subset selection or discretization.

Keywords

Mutual Information Posteriori Probability Feature Subset Selection Kernel Component Intelligent Information System 
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

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    Hall, M.A., Smith, L.A.: Feature subset selection: A correlation based filter approach. In: Proceeding of the Fourth International Conference on Neural Information Processing and Intelligent Information Systems, pp. 855–858 (1997)Google Scholar
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    Páerez, A., Larrañaga, P., Inza, I.: Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes. International Journal of Approximate Reasoning (in press, 2006)Google Scholar
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    Silverman, B.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aritz Páerez
    • 1
  • Pedro Larrañaga
    • 1
  • Iñaki Inza
    • 1
  1. 1.Intelligent Systems Group, Computer Science and A.I Dept.University of The Basque CountrySpain

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