A new PC-PSO algorithm for Bayesian network structure learning with structure priors

Bayesian network structure learning is the basis of parameter learning and Bayesian inference. However, it is a NP-hard problem to find the optimal structure of Bayesian networks because the computational complexity increases exponentially with the increasing number of nodes. Hence, numerous algorithms have been proposed to obtain feasible solutions, while almost all of them are of certain limits. In this paper, we adopt a heuristic algorithm to learn the structure of Bayesian networks, and this algorithm can provide a reasonable solution to combine the PC and Particle Swarm Optimization (PSO) algorithms. Moreover, we consider structure priors to improve the performance of our PC-PSO algorithm. Meanwhile, we utilize a new mutation operator called Uniform Mutation by Addition and Deletion (UMAD) and a crossover operator called Uniform Crossover. Experiments on different networks show that the approach proposed in this paper has achieved better Bayesian Information Criterion (BIC) scores than other algorithms.

Automated summary

This paper presents a heuristic algorithm combining PC and PSO algorithms for learning the structure of Bayesian networks, considering structure priors to enhance algorithm performance, and introducing new mutation and crossover operators. Experimental results demonstrate that the proposed approach outperforms other algorithms in terms of Bayesian Information Criterion (BIC) scores.