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Applying Bayesian Approach to Decision Tree

  • Yatong Zhou
  • Taiyi Zhang
  • Zhigang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Applying Bayesian approach to decision tree (DT) model, and then a Bayesian-inference-based decision tree (BDT) model is proposed. For BDT we assign prior to the model parameters. Together with observed samples, prior are converted to posterior through Bayesian inference. When making inference we resort to simulation methods using reversible jump Markov chain Monte Carlo (RJMCMC) since the dimension of posterior distribution is varying. Compared with DT, BDT enjoys the following three advantages. Firstly, the model’s learning procedure is implemented with sampling instead of a series of splitting and pruning operations. Secondly, the model provides output that gives insight into different tree structures and recursive partition of the decision space, resulting in better classification accuracy. And thirdly, the model can indicate confidence that the sample belongs to a particular class in classification. The experiments on music style classification demonstrate the efficiency of BDT.

Keywords

Support Vector Machine Decision Tree Bayesian Inference Bayesian Approach Leaf Node 
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

  • Yatong Zhou
    • 1
  • Taiyi Zhang
    • 1
  • Zhigang Chen
    • 1
  1. 1.Dept. Information and Communication Engineering, Xi’an Jiaotong University, 710049 Xi’anChina

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