Refining Bayesian Network Structure When New Data Has Different Set of Attributes
نویسندگان
چکیده
Refinement of Bayesian network structure is a topic that becomes more and more relevant. Some work have been done there, but one problem has not been considered yet – what to do when new data has less or more attributes than the existing model. In both cases data contains important knowledge and every effort must be made in order to extract it. A merging algorithm capable to deal with situations when new data has different set of attributes is introduced in this paper in order to expand flexibility of the Bayesian network structure refinement methods.
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