Adjusted probability Naive Bayesian induction

  • Geoffrey I. Webb
  • Michael J. Pazzani
Scientific Track
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1502)


Naive Bayesian classifiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classification tasks is surprisingly competitive in comparison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the naive Bayesian classifier. A numeric weight is inferred for each class. During discriminate classification, the naive Bayesian probability of a class is multiplied by its weight to obtain an adjusted value. The use of this adjusted value in place of the naive Bayesian probability is shown to significantly improve predictive accuracy.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Geoffrey I. Webb
    • 1
  • Michael J. Pazzani
    • 2
  1. 1.School of Computing and MathematicsDeakin UniversityGeelongAustralia
  2. 2.Department of Information and Computer ScienceUniversity of California, IrvineIrvineUSA

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