Abstract：Learning the structure of a Bayesian network from data may be time expensive due to huge search space. Because a Bayesian network contains causal semantics, experts can use their knowledge to confirm cause and effect among variables. In this paper, experts’ opinions are collected and combined using DempsterShafer evidence theory. The network structures without semantics are eliminated, then learning network from data is continued. This method fuses expert knowledge which is used to reduce search space with data to construct a Bayesian network. It avoids the subjective bias of single expert. The experimental results show that this method can improve learning efficiency.
杨善林，胡笑旋，毛雪岷. 融合知识和数据的贝叶斯网络构造方法*[J]. 模式识别与人工智能, 2006, 19(1): 31-34.
YANG ShanLin, HU XiaoXuan, MAO XueMin. A Method of Bayesian Network Construction Combining Knowledge and Data. , 2006, 19(1): 31-34.
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