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...
- 1.Aggarwal JK, Ghosh J, Nair D, Taha I. A comparative study of three paradigms for object recognition – Bayesian statistics, neural networks, and expert systems. In: Advances in image understanding: a festschrift for Azriel Rosenfeld. Washington, DC: IEEE Computer Society Press; 1996. p. 241–62.Google Scholar
- 3.Domingos P, Pazzani M. Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Proceedings of the 13th International Conference on Machine Learning; 1996. p. 105–12.Google Scholar
- 5.Dumais S, Platt J, Heckerman D, Sahami M. Inductive learning algorithms and representations for text categorization. In: Proceedings of the International Conference on Information and Knowledge Management; 1998.Google Scholar
- 8.Heckerman D. Probabilistic similarity networks. ACM doctoral dissertation award series. Cambridge, MA: MIT Press; 1991.Google Scholar
- 9.Jaeger M. Probabilistic classifiers and the concepts they recognize. In: Proceedings of the 20th International Conference on Machine Learning; 2003. p. 266–73.Google Scholar
- 10.Jensen FV. An introduction to Bayesian networks. New York: Springer; 1996.Google Scholar
- 11.Keogh E, Pazzani M. Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In: Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics; 1999.Google Scholar
- 12.Kononenko I, Bratko I, Kukar M. Application of machine learning to medical diagnosis. In: Machine learning, data mining and knowledge discovery: methods and applications. New York: Wiley; 1998.Google Scholar
- 13.Langley P, Iba W, Thompson K. An analysis of Bayesian classifiers. In: Proceedings of the 10th National Conference on Artificial Intelligence. 1992. p. 3–8.Google Scholar
- 14.Pearl J. Probabilistic reasoning in intelligenet systems: networks of plausible inference. San Mateo: Morgan Kaufmann; 1988.Google Scholar
- 15.Wright S. Correlation and causation. J Agric Res. 1921;20(7):557–85.Google Scholar