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Discretisation Effects in Naive Bayesian Networks

  • Roel Bertens
  • Linda C. van der Gaag
  • Silja Renooij
Part of the Communications in Computer and Information Science book series (CCIS, volume 299)

Abstract

Naive Bayesian networks are often used for classification problems that involve variables of a continuous nature. Upon capturing such variables, their value ranges are modelled as finite sets of discrete values. While the output probabilities and conclusions established from a Bayesian network are dependent of the actual discretisations used for its variables, the effects of choosing alternative discretisations are largely unknown as yet. In this paper, we study the effects of changing discretisations on the probability distributions computed from a naive Bayesian network. We demonstrate how recent insights from the research area of sensitivity analysis can be exploited for this purpose.

Keywords

Bayesian Network Feature Variable Sensitivity Function Class Variable Joint Probability Distribution 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roel Bertens
    • 1
  • Linda C. van der Gaag
    • 1
  • Silja Renooij
    • 1
  1. 1.Department of Information and Computing SciencesUtrecht UniversityThe Netherlands

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