Naïve Bayesian Tree Pruning by Local Accuracy Estimation

  • Zhipeng Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Naïve Bayesian Tree is a high-accuracy classification method by combining decision tree and naïve Bayes together. It uses averaged global accuracy as the measurement of goodness in the induction process of the tree structure, and chooses the local classifier that is most specific for the target instance to make the decision. This paper mainly introduces a pruning strategy based on local accuracy estimation. Instead of directly using the most specific local classifier (mostly the classifier in a leaf node) to making classification in NBTree, our pruning strategy uses the measurement of local accuracy to guide the selection of local classifier for decision. Experimental results manifest that this pruning strategy is effective, especially for the NBTree with relatively more nodes.


Leaf Node Child Node Local Accuracy Local Classifier Pruning Strategy 
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

  • Zhipeng Xie
    • 1
  1. 1.Department of Computing and Information TechnologyFudan UniversityShanghaiP.R. China

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