Naive Bayes is a simple, computationally efficient and remarkably accurate
approach to classification learning. These properties have led to its wide deployment
in many online applications. However, it is based on an assumption that all
attributes are conditionally independent given the class. This assumption leads
to decreased accuracy in some applications. AODE overcomes the attribute independence
assumption of naive Bayes by averaging over all models in which all attributes
depend upon the class and a single other attribute. The resulting classification
learning algorithm for nominal data is computationally efficient and achieves
very low error rates.