Abstract:

Introducing expert knowledge is the main method of Bayesian networks(BN) modeling from small data set. The results and performance of algorithm are affected by the correctness of the expert knowledge. Therefore, considering the correctness of the expert knowledge, the problem of BN learning is studied. First of all, the structural constraints model based on joint probability distribution is proposed to represent the expert knowledge, and then the Bayesian information criterions (BIC) is improved by combining with the constraint model. Finally, the K2 algorithm is used for learning BN. The experimental results show that the proposed algorithm can not only introduce the expert knowledge into the process of BN learning to improve the learing effect, but also have some adaptability to the not entirely correct expert knowledge.