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Information Technology Journal
  Year: 2006 | Volume: 5 | Issue: 3 | Page No.: 540-545
DOI: 10.3923/itj.2006.540.545
 
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Research on Learning Bayesian Networks by Particle Swarm Optimization

Xing-Chen Heng, Zheng Qin, Xian-Hui Wang and Li-Ping Shao

Abstract:
A new approach to learning Bayesian networks (Bns) was proposed in this study. This approach was based on Particle Swarm Optimization (PSO). We start by giving a fitness function to evaluate possible structure of BN. Next, the definition and encoding of the basic mathematical elements of PSO were given and the basic operations of PSO was designed which provides guarantee of convergence. Next, full samples for the training set and test set are generated from a known original Bayesian network with probabilistic logic sampling. After that, the structure of BN was learned from complete training set using improved PSO algorithm steps. Finally, the simulation experimental results also demonstrated sthis new approach’s efficiency.
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How to cite this article:

Xing-Chen Heng, Zheng Qin, Xian-Hui Wang and Li-Ping Shao, 2006. Research on Learning Bayesian Networks by Particle Swarm Optimization. Information Technology Journal, 5: 540-545.

DOI: 10.3923/itj.2006.540.545

URL: https://scialert.net/abstract/?doi=itj.2006.540.545

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