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- W2351246722 abstract "The Naive Bayesian classification based on the Bayesian theorem is very popular. It approximates the Bayesian theorem by the assumption of class conditional independence. On this basis , Bayesian networks compensate for the assumption by adopting graphical models. On the other hand, it implies that the NP problem will occur in the process of classification. This paper adopts a middle way: It combines association rules with the ABN classification to construct a Bayesian classifier. It improves the independence assumption. Meanwhile, it avoids the NP problem. Finally we use our experimental results to show that it is better than the Naive Bayesian classifier in most fields." @default.
- W2351246722 created "2016-06-24" @default.
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- W2351246722 date "2005-01-01" @default.
- W2351246722 modified "2023-09-23" @default.
- W2351246722 title "The Structure of ABN Classifiers Based on Association Rules" @default.
- W2351246722 hasPublicationYear "2005" @default.
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