A method of Bayesian network construction combining knowledge and data

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 Dempster-Shafer 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.