Structure Learning of Bayesian Networks by Finding the Optimal Ordering

Ordering-based search methods have advantages over graph-based search methods for structure learning of Bayesian networks in terms of both efficiency and accuracy. With the aim of further increasing the accuracy of ordering-based search methods, we propose to increase the search space, which can facilitate escaping from local optima. We present our search operators with majorizations, which are easy to implement. Experiments demonstrate that the proposed algorithm achieves significant accuracy improvement and exhibits high efficiency at the same time on both synthetic and real data sets. With regard to further improve the algorithm efficiency on learning large scale networks, we discuss a solution at the end of the paper.