The modeling method with bayesian networks and its application in the threat assessment under small data sets

Bayesian network is one of the main tools for data mining.In such cases as large equipment fault diagnosis,geological disaster forecast,operational decision,etc,good results are expected to achieve based on small data sets.Therefore,this article focuses on the problem of learning Bayesian network from small data sets.Firstly,the structure constraint model based on the probability distribution of the connection was built.Then,the improved-Bayesian Dirichlet-binary particle swarm optimization algorithm was proposed.Secondly,the monotonicity parameter constraint model was defined and the monotonicity constraint estimation algorithm was proposed.Finally,the proposed algorithm was applied to construct the threat assessment model.Then,the model was used for reasoning with the variable elimination method.Experimental results reveal that the structure learning algorithm outperforms classical binary particle swarm optimization algorithm and the parameter learning method surpasses maximum likelihood estimation,isotonic regression and convex optimization method for small data sets.The threat assessment model is also proved to be effective.