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Bayesian network structure learning based on restricted particle swarm optimization. (Chinese. English summary) Zbl 1265.68133
Summary: Bayesian network structure learning is one of the main research topics in the field of data mining and knowledge discovery, when the search space of the network structure is relatively big, some algorithms have the problems that the convergent speed is slow and the accuracy is poor.
In this paper, a kind of information theory combining particle swarm optimization algorithm is put forward, which uses mutual information to limit particle initialization, and makes the particle swarm optimization algorithm converge in a relatively short period of time. An ASIA network is applied as the simulation model and the proposed algorithm is compared with the K2 algorithm. Experimental results show that the proposed algorithm can rapidly and accurately learn Bayesian network structures.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
68T30 Knowledge representation
68P15 Database theory
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