Bayesian Network Structure Learning

Structure learning is a very important problem in the field of Bayesian networks (BNs). It is also an active research area for more than two decades; therefore, many approaches have been proposed in order to find an optimal structure based on training samples. In this book, we shortly introduce BNs and structure learning in them; then, a Particle Swarm Optimization (PSO)-based algorithm is proposed to solve the BN structure learning problem. In the proposed algorithm, which named BNC-PSO (Bayesian Network Construction algorithm using PSO), edge inserting/deleting is employed to make the particles have the ability to achieve the optimal solution, while a cycle removing procedure is used to prevent the generation of invalid solutions. The theorem of Markov chain is also used to prove the global convergence of the proposed algorithm. Finally, some experiments are designed to evaluate the performance of the proposed PSO-based algorithm. Experimental results indicate that BNC-PSO is worthy of being studied in the field of BNs construction. Meanwhile, it can significantly increase nearly 15% in the scoring metric values, comparing with other optimization-based algorithms.