An interactive approach for Bayesian network learning using domain/expert knowledge

Using domain/expert knowledge when learning Bayesian networks from data has been considered a promising idea since the very beginning of the field. However, in most of the previously proposed approaches, human experts do not play an active role in the learning process. Once their knowledge is elicited, they do not participate any more. The interactive approach for integrating domain/expert knowledge we propose in this work aims to be more efficient and effective. In contrast to previous approaches, our method performs an active interaction with the expert in order to guide the search based learning process. This method relies on identifying the edges of the graph structure which are more unreliable considering the information present in the learning data. Another contribution of our approach is the integration of domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure.

• A novel interactive methodology to integrate domain/expert knowledge when learning Bayesian networks from data.
• This methodology interactively requests information to the expert for guiding the learning process.
• It only requests to the expert the knowledge that can not be inferred with the information present in the data.
• An specific multi-level stochastic search is employed for this purpose.
• The quantity of domain/expert knowledge is minimized while the quality of the inferences is significantly boosted.