Converting a Naive Bayes Models with Multi-valued Domains into Sets of Rules

  • Bartłomiej Śnieżyński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


Nowadays, several knowledge representation methods are being used in knowledge based systems, machine learning, and data mining. Among them are decision rules and Bayesian networks. Both methods have specific advantages and disadvantages. A conversion method would allow to exploit advantages of both techniques. In this paper an algorithm that converts Naive Bayes models with multi-valued attribute domains into sets of rules is proposed. Experimental results show that it is possible to generate rule-based classifiers, which have relatively high accuracy and are simpler than original models.


Decision Rule Bayesian Network Medical Informatics Default Rule Bayesian Belief Network 
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 2006

Authors and Affiliations

  • Bartłomiej Śnieżyński
    • 1
  1. 1.Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland

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