A Bayesian Network Structure Learning Algorithm Based on the Combination of PSO and Sub-graph Decomposition

DOI

10.2991/icemc-16.2016.194How to use a DOI?

Keywords

Bayesian network; Structure learning; Maximal prime decomposition technology; Particle swarm optimization algorithm

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/icemc-16.2016.194
ID  - Feng2016/05
ER  -