Structure Learning for Bayesian Networks Using the Physarum Solver

A novel structure learning algorithm for Bayesian Networks based on the Phyasrum Solver is introduced. First, the algorithm calculates pair wise correlation coefficients in the dataset. Within an initially fully connected Physarum-Maze, the length of the connections is given by the inverse correlation coefficient between the connected nodes. Then, the shortest indirect paths between each two nodes is determined using the Physarum Solver. In each iteration, a score of the surviving edges is increased. Based on that score, the highest ranked connections are combined to form a Bayesian Network. The novel Physarum Learner method is evaluated with different configurations and compared to the LAGD Hill Climber showing comparable performance regarding the quality of training results and increased time efficiency for large datasets.