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This paper proposes a novel naïve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a distance...
General Bayesian network classifier (GBNC) contains only features necessary for classification, so an ideal structure learning solution is to learn GBNC without having to learn the whole Bayesian network (BN). A local search based algorithm called LAS-GBNC is proposed. Given faithfulness assumption, LAS-GBNC relies on the information about each variable's appearance in the so-called d-separator(cut...
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