Decision Tree Model Based on Bayesian
Inference
Zhou Yatong,Zhang Taiyi,Lu Zhaogan
(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an
710049, China)
Abstract: Focusing on the problem that conventional decision tree (DT) model lacks of a
probabilistic background, the Bayesian inference was introduced into DT, and thus a
decision tree (BDT) model based on Bayesian inference was proposed. Under the premise that
the prior and likehood of contained parameters needed to be determined has been assumed,
the posterior of parameters are obtained through Bayesian inference. Then the posterior is
sampled by using reversible jump Markov chain Monte Carlo algorithm, and finally the
confidence level of the samples belonged to certain class is solved to avoid any arbitrary
decision. In BDT model, the splitting and pruning is substituted by sampling, both are
intuitive and flexible, and different tree structures and recursive partition schemes are
considered so as to increase the accuracy rate of classification. The experimental results
show that the average classification accuracy is improved by 1.7%-3.5% compared to DT
model.
Keywords: decision tree;Bayesian inference;reversible jump Markov chain Monte
Carlo;classifi- cation accuracy rate;recursive partition