Semi-naive bayesian classifier

  • Igor Kononenko
Part 3: Numeric And Statistical Approaches
Part of the Lecture Notes in Computer Science book series (LNCS, volume 482)


In the paper the algorithm of the ‘naive’ Bayesian classifier (that assumes the independence of attributes) is extended to detect the dependencies between attributes. The idea is to optimize the tradeoff between the ‘non-naivety’ and the reliability of approximations of probabilities. Experiments in four medical diagnostic problems are described. In two domains where by the experts opinion the attributes are in fact independent the semi- naive Bayesian classifier achieved the same classification accuracy as naive Bayes. In two other domains the semi-naive Bayesian classifier slightly outperformed the naive Bayesian classifier.


machine learning Bayesian classifier approximations of probabilities (in)dependence of events 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Igor Kononenko
    • 1
  1. 1.Faculty of electrical & computer engineeringUniversity of LjubljanaLjubljanaYugoslavia

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