Bayesian networks (BNs) are widely used as a data analysis tool in diverse areas, including finance, medicine, and sports. A standard data analysis methodology is to use the well-known score-and-search approach to learn a set of possible Bayesian networks and then to perform model averaging with thresholding to identify features such as edges between variables with high confidence. A fundamental step in the methodology is to select the threshold, as the value selected has broad implications for the success of the analysis. However, the problem of selecting a good threshold in Bayesian network structure learning has received limited attention in the literature. In this paper, we identifying an important shortcoming in a widely used threshold selection method. We then propose a simple transfer learning approach for maximizing target metrics and selecting a threshold that can be generalized from proxy datasets to the target dataset and show on an extensive set of benchmarks that it can perform significantly better than previous approaches.
Article ID: 2022L16
Publisher: Canadian Artificial Intelligence Association