Affiliations: [a] Institute of Health Statistics and Intelligent Analysis, School of Public Health, Lanzhou University, Lanzhou, Gansu, P. R. China
| [b] Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, P. R. China
| [c] School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, P. R. China
Correspondence:
[*]
Corresponding author. Dr. Marcel M. Verbeek, TML, r.830, Department of Neurology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands. Tel.: +31 24 36 14567; Fax: +31 2436 68754; E-mail: gaowl2019@sina.com.
Note: [] http://orcid.org/0000-0001-6845-4541.
Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy.
Keywords: Keywords. Bayesian network, structure learning, HC algorithm, PSO algorithm, DE algorithm
