Integrating expert knowledge for Bayesian network structure learning based on intuitionistic fuzzy set and Genetic Algorithm

The Bayesian network (BN) structure learning from the observational data has been proved to be a NP-hard problem. The expert knowledge is beneficial to determine the BN structure, especially when the data are scarce and the related variables are huge in the researched domain. In this paper, we propose a new BN structure learning method by integrating expert knowledge. On the one hand, to improve the performance of expert knowledge usage, the intuitionistic fuzzy set (IFS) is introduced to express and integrate the expert knowledge. The determination of BN priori structure is transformed into the group decision making problem. On the other hand, the improved Bayesian information criterion score function and the Genetic Algorithm search algorithm are used to obtain the most suitable structure under the constraints from the priori structure. Some experiments demonstrate the validity of proposed scheme and compare the performance with the existing research results. The obtained BN structure owns better performance. The more the quantity of expert knowledge is, the better the performance of BN structure learning would be. Finally, the proposed method is applied to the thickening process of gold hydrometallurgy to solve the practical problem