Improving Acyclic Selection Order-Based Bayesian Network Structure Learning

An effective approach for learning Bayesian network structures in large domains is to perform a local search on the space of topological orderings. As with most local search approaches, the quality of the procedure depends on the initialization strategy. Usually, a simple random initialization is adopted. Perez and Mauá developed initialization heuristics that were empirically shown to improve the overall performance of order-based structure learning. Recently, Scanagatta et al. proposed replacing the search for a directed acyclic graph in order-based learning with a procedure that considers also order-incompatible structures. Their procedure covers a larger space of structures without small computational overhead, which often leads to improved performance. As with standard order-based learning, Scanagatta et al. recommended initializing their algorithm with a randomly generated ordering. A natural improvement for this approach would be then to consider better initialization heuristics. In this work we propose a new initialization heuristic that takes into account the idiosyncrasies of Scanagatta et al.’s approach. Experiments with real-world data sets indicate that the combination of this new heuristic and Scanagatta et al.’s orderbased search outperforms other order-based methods.