Learning Optimal Bayesian Networks Using A* Search
Changhe Yuan, Brandon Malone and Xiaojian Wu
This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* search algorithm is introduced to solve the problem. With the guidance of a consistent heuristic, the algorithm learns an optimal Bayesian network by only searching the most promising parts of the solution space. Empirical results show that the A* search algorithm significantly improves the time and space efficiency of existing methods on most of the benchmark datasets tested in this paper.