A Bayesian Network Structure Learning Algorithm Based on the Combination of PSO and Sub-graph Decomposition
- https://doi.org/10.2991/icemc-16.2016.194How to use a DOI?
- Bayesian network; Structure learning; Maximal prime decomposition technology; Particle swarm optimization algorithm
In this paper we mainly focus on how to reduce the searching spaces when accelerating the efficiency of searching for the best Bayesian network structure and a corresponding algorithm named MPD-PSO based on the combination of MPD(maximal prime decomposition technology) and PSO(particle swarm optimization algorithm) was proposed. In the algorithm we proposed, we firstly use the MBDA(Markov Boundary Discovery Algorithm) algorithm to achieve the Markov boundary, and construct the undirected independent graph, then we decomposed the large undirected independent graph into several maximal prime sub-graphs by using the MPD algorithm, therefore we transform a high-dimensional structure learning problem into a low-dimensional structure(sub-graph structure) learning problem and reduce the searching space relatively. After that we use the PSO algorithm to learn the sub-graph, and we merge the learned sub-structure by correcting the wrong edges to achieve the best Bayesian structure. We validate the proposed algorithm by using Asia and Alarm network, and the results verify the superiority of the proposed algorithm in learning effect and running time over the GA(Genetic Algorithm) and PSO algorithms.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Shaorong Feng PY - 2016/05 DA - 2016/05 TI - A Bayesian Network Structure Learning Algorithm Based on the Combination of PSO and Sub-graph Decomposition BT - Proceedings of the 2016 International Conference on Education, Management and Computer Science PB - Atlantis Press SP - 986 EP - 992 SN - 1951-6851 UR - https://doi.org/10.2991/icemc-16.2016.194 DO - https://doi.org/10.2991/icemc-16.2016.194 ID - Feng2016/05 ER -