Abstract: In view of learning the Bayesian network under the condition of the small sample data,this paper firstly made use of kernel density estimation to expand the small scale sample data,then adopted the cloud theory-based genetic algotithm to learn the structure of Bayesian network.In order to improve the effect of data expanding,the paper discussed the way of improving the density function and its window breadth.At the same time,the cloud theory was combined with genetic algotithm.We changed crosses rate and variation rate properly,avoided the problem of looking an excellent answer in a part.Simulation results show that the algorithm is effective and practical.()

Key words: Small sample data,Bayesian network,Structure learning,Kernel density estimation,Cloud theory-based genetic algorithm