关键词: 贝叶斯网络, 专家知识, 因果关系, 证据理论

Abstract: The efficiency of Bayesian networks structure learning algorithm based on data learning is low due to the big search space. Domain experts determine the inherent causal relationship in network structure according to their own experience knowledge. By collecting different expert opinion, and using the evidence theory to synthesize, it determines the part of causality, removes some meaningless causal relationship, then uses the common learning algorithms to learn, reduces the search space, enhances the efficiency of the algorithm. The experimental results show that the Bayesian network construction method based on the expert knowledge fusion limits the search terms of the learning algorithm by expert knowledge, effectively reduces the search space, uses the evidence theory to synthesize much expert knowledge, prevents a single expert subjective and one-sidedness, can effectively improve the learning efficiency.

Key words: Bayesian network, expert knowledge, causality, evidence theory