【摘要】 针对直接基于小数据集贝叶斯网络结构学习不可靠,以及目前对小数据集的处理只强调扩展而忽略对扩展数据的修正等,提出了将扩展与修正相结合的小数据集处理机制,以及在此基础上的基于结点排序和局部打分-搜索的贝叶斯网络结构学习方法,可不需要完全结点顺序的先验知识,但能够结合专家的部分结点顺序信息,实验结果显示了这种方法的有效性和可靠性。更多还原

【Abstract】 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.更多还原