In classification, the objective is to build a classifier that takes an unlabeled example and assigns it to a class. Bayesian classification does this by modeling the probabilistic relationships between the attribute set and the class variable. Based on the modeled relationships, it estimates the class membership probability of the unseen example.
The foundation of Bayesian classification goes back to Reverend Bayes himself . The origin of Bayesian belief nets can be traced back to . In 1965, Good  combined the independence assumption with the Bayes formula to define the Naïve Bayes Classifier. Duda and Hart  introduced the basic notion of Bayesian classification and the naïve Bayes representation of joint distribution. The modern treatment and development of Bayesian belief networks is attributed to Pearl . Heckerman  later reformulated the Bayes results and defined the probabilistic similarity networks...
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