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Several Computational Studies About Variable Selection for Probabilistic Bayesian Classifiers

  • Adriana BroginiEmail author
  • Debora Slanzi
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The Bayesian network can be considered as a probabilistic classifier with the ability of giving a clear insight into the structural relationships in the domain under investigation. In this paper we use some methodologies of feature subset selection in order to determine the relevant variables which are then used for constructing the Bayesian network. To test how the selected methods of feature selection affect the classification, we consider several Bayesian classifiers: Naïve Bayes, Tree Augmented Naïve Bayes and the general Bayesian network, which is used as benchmark for the comparison.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of PadovaPadovaItaly

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