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Structure Learning of Bayesian Networks Based on Discrete Binary Quantum-Behaved Particle Swarm Optimization Algorithm

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4 Author(s)
Jing Zhao ; Sch. of Inf. Technol., Jiangnan Univ. Wuxi, Wuxi, China ; Jun Sun ; Wenbo Xu ; Di Zhou

Searching the best Bayesian Network is an NP-hard problem. When the number of variables in Bayesian Network is large, the process of searching is likely to fall into premature convergence and return a local optimal network structure. A new approach for Bayesian Networks structure learning, which is based on the discrete Binary Quantum-behaved Particle Swarm Optimization algorithm, is introduced. The proposed approach is used to find a Bayesian Network that best matches sample data sets. For evaluating the best matching degree between Bayesian Network and sample data sets, Bayesian Information Criterion score is proposed. Then ASIA network, a benchmarks of Bayesian Networks, is used to test the new approach. The results of experiment show that the proposed technique converges more rapidly than other evolutionary computation methods.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:6 )

Date of Conference:

14-16 Aug. 2009