Application of structural EM algorithm to learning Bayesian networks for small sample
Weiyi Liu · Yunnan Daxue xuebao. Shehui kexue ban · 2007
Existing data sets of cases can significantly reduce the knowledge engineering effort required to learning Bayesian networks.When a data set is small,many conditioning cases are represented by too few or no data records and they do not offer sufficient basis for learning Bayesian networks.It is proposed a method that combines data revising and the Bayesian Structural EM algorithm.Experimental results show that this method is effective in learning Bayesian networks from small data set.