Learning Bayesian Network Structure from Small Data Set
小数据集的贝叶斯网络结构学习

It is incredible to learn Bayesian network structure directly from small data set. For improving the reliability, many methods of extending small data set have been developed, but the revision of extended data is neglected. In this paper, extending small data set is combined with revising extended data to upswing the data reliability. A directed tree is built from the small data set and variables are sorted according to it. On the basis of the variable order, a Bayesian network structure can be established based on the local search and scoring method. This method dose not need the prior knowledge of the variable order, but the partial order information of expert can be used properly. Experimental results show that this method can effectively learn Bayesian network structure from a small data set.