Learning Bayesian Networks Structure from Small Data Set in Operational Risk Analysis
Shuangcheng Wang · Journal of systems management · 2008
At present,the methods of learning Bayesian networks structure need a large number of data with high quality.The algorithms have low efficiency and reliability.But it is very difficult to accumulate many reliable examples in operational risk management.In this paper,a new method of learning Bayesian networks structure from small data set is presented based on basic dependency relationship between variables,basic structure between nodes,d-separation criterion and dependency analysis method.It can effectively avoid the problems above. The experiments and analysis are done by using stimulant and real-world dataset.Experimental results show that this method can effectively learn Bayesian network structure from small data set.