A Linear-Bayes Classifier

  • João Gama
Conference paper

DOI: 10.1007/3-540-44399-1_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1952)
Cite this paper as:
Gama J. (2000) A Linear-Bayes Classifier. In: Monard M.C., Sichman J.S. (eds) Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 1952. Springer, Berlin, Heidelberg


Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Although its limitations with respect to expressive power, this procedure has a surprisingly good performance in a wide variety of domains, including many where there are clear dependencies between attributes. In this paper we address its main perceived limitation - its inability to deal with attribute dependencies. We present Linear Bayes that uses, for the continuous attributes, a multivariate normal distribution to compute the require probabilities. In this way, the interdependencies between the continuous attributes are considered. On the empirical evaluation, we compare Linear Bayes against a naive- Bayes that discretize continuous attributes, a naive-Bayes that assumes a univariate Gaussian for continuous attributes, and a standard Linear discriminant function. We show that Linear Bayes is a plausible algorithm, that competes quite well against other well established techniques.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • João Gama
    • 1
  1. 1.LIACC, FEPUniversity of PortoPortoPortugal

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