Naive Bayes Classifiers Learned from Incomplete Data
Qiao Zhufeng · 2006
Bayes networks,as directed acyclic graphs of causal structure of attributes,have become the efficient method for processing incomplete data.However,most Bayesian network classifiers are learned from complete data and the world is rarely fully observable and data is often incomplete.So constructing Bayesian network classifiers from incomplete data is an important and challenging problem.An efficient method for constructing Bayesian network classifiers from incomplete data based on BC method and EM algorithm is presented.Experimental results show its validity.