Learning the naive Bayes classifier with optimization models - Taheri, Sona; Mammadov, Musa (Zielona Góra: Uniwersytet Zielonogórski)
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations.
Keywords: Bayesian networks; naive Bayes classifier; optimization; discretization