Synonyms
Bayes classifier
Definition
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.
Historical Background
The foundation of Bayesian classification goes back to Reverend Bayes himself [2]. The origin of Bayesian belief nets can be traced back to [15]. In 1965, Good [4] combined the independence assumption with the Bayes formula to define the Naïve Bayes Classifier. Duda and Hart [14] 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 [8]. Heckerman [13] later reformulated the Bayes results and defined the probabilistic similarity networks...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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.
Bayes T. An essay towards solving a problem in the doctrine of chances. Philos Trans R Soc. 1763;53:370–418.
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.
Duda RO, Hart PE. Pattern classification and scene analysis. New York: Wiley; 1973.
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.
Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997;29:131–63.
Good IJ. The estimation of probabilities: an essay on modern Bayesian methods. Cambridge, MA: MIT Press; 1965.
Heckerman D. Probabilistic similarity networks. ACM doctoral dissertation award series. Cambridge, MA: MIT Press; 1991.
Jaeger M. Probabilistic classifiers and the concepts they recognize. In: Proceedings of the 20th International Conference on Machine Learning; 2003. p. 266–73.
Jensen FV. An introduction to Bayesian networks. New York: Springer; 1996.
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.
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.
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.
Pearl J. Probabilistic reasoning in intelligenet systems: networks of plausible inference. San Mateo: Morgan Kaufmann; 1988.
Wright S. Correlation and causation. J Agric Res. 1921;20(7):557–85.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Hsu, W. (2018). Bayesian Classification. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_556
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_556
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering