Mining Bayesian Network Structure for Large Sets of Variables
A well-known problem with Bayesian networks (BN) is the practical limitation for the number of variables for which a Bayesian network can be learned in reasonable time. Even the complexity of simplest tree-like BN learning algorithms is prohibitive for large sets of variables. The paper presents a novel algorithm overcoming this limitation for the tree-like class of Bayesian networks. The new algorithm space consumption grows linearly with the number of variables n while the execution time is proportional to n ln(n), outperforming any known algorithm. This opens new perspectives in construction of Bayesian networks from data containing tens of thousands and more variables, e.g. in automatic text categorization.
KeywordsBayesian Network Edge Node Bayesian Belief Network Edge Tree Edge Removal
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