Bayes Classifier

Part of the Undergraduate Topics in Computer Science book series (UTICS, volume 0)


Bayes classifier is popular in pattern recognition because it is an optimal classifier. It is possible to show that the resultant classification minimises the average probability of error. Bayes classifier is based on the assumption that information about classes in the form of prior probabilities and distributions of patterns in the class are known. It employs the posterior probabilities to assign the class label to a test pattern; a pattern is assigned the label of the class that has the maximum posterior probability. The classifier employs Bayes theorem to convert the prior probability into posterior probability based on the pattern to be classified, using the likelihood values. In this chapter, we will introduce some of the important notions associated with the Bayes classifier.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Duda, R. O.,P. E. Hart, andD. G. Stork. Pattern Classification. Second Edition. Wiley-Interscience. 2001.Google Scholar
  2. 2.
    Russell, S. and P. Norvig. Artificial Intelligence: A Modern Approach. Pearson India. 2003.Google Scholar
  3. 3.
    Domingos, P. and M. Pazzani On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29: 103–130. 1997.zbMATHCrossRefGoogle Scholar
  4. 4.
    Pearl, J. Probabilistic Reasoning in Intelligent Systems. Morgan Kauffman. 1988.Google Scholar
  5. 5.
    Rish, I. An empirical study of the naive Bayes classifier. IJCAI Workshop on Empirical Methods in Artificial Intelligence. 2001.Google Scholar
  6. 6.
    Heckerman, D. Bayesian networks for knowledge discovery. In Advances in Knowledge Discovery and Data Mining edited by U. M. Fayyad, G. P. Shapiro, P. Smyth, and R. Uthurusamy. MIT Press. 1996.Google Scholar
  7. 7.
    Tan, P. N., M. Steinbach, and V. Kumar. Introduction to Data Mining. Pearson India. 2007.Google Scholar
  8. 8.
    Neapolitan, R. E. Learning Bayesian Networks. Upper Saddle River, NJ: Prentice Hall. 2003.Google Scholar
  9. 9.
    Bishop, C. M. Neural Networks for Pattern Recognition. New Delhi: Oxford University Press. 2003.Google Scholar

Copyright information

© Universities Press (India) Pvt. Ltd. 2011

Authors and Affiliations

  1. 1.Dept. of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

Personalised recommendations