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2015 | OriginalPaper | Chapter

4. Bayesian Classifiers

Author : Luis Enrique Sucar

Published in: Probabilistic Graphical Models

Publisher: Springer London

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Abstract

This chapter covers Bayesian classifiers. After a brief introduction to the classification problem, the Naive Bayesian classifier is presented, as well as its main variants: TAN and BAN. Then the semi-Naive Bayesian classifier is described. A multidimensional classifier may assign several classes to the same object. Two alternatives for multidimensional classification are analyzed: the multidimensional Bayesian network classifier and the Bayesian chain classifier. Then an introduction to hierarchical classification is presented. The chapter concludes by illustrating the application of Bayesian classifiers in two domains: skin pixel detection in images and drug selection for HIV treatment.

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previous chapter Graph Theory
next chapter Hidden Markov Models
Footnotes
1
For an introduction and comparison of different types of classifiers we refer the interested reader to [10].
 
2
The posterior probabilities of the classes will be affected by a constant as we are not considering the denominator in Eq. (4.6), that is, they will not add to one; however, they can be easily normalized by dividing each one by the sum for all classes.
 
3
We will cover parameter estimation in detail in the chapter on Bayesian Networks.
 
4
This assumes that the misclassification cost is the same for all classes; if these costs are not the same, the class the minimizes the misclassification cost should be selected.
 
5
Bayesian networks are introduced in Chap. 7.
 
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Sucar, L.E., Bielza, C., Morales, E., Hernandez, P., Zaragoza, J., Larrañaga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognit. Lett. 41, 14–22 (2014)CrossRef
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Metadata
Title
Bayesian Classifiers
Author
Luis Enrique Sucar
Copyright Year
2015
Publisher
Springer London
Book
Probabilistic Graphical Models

Print ISBN: 978-1-4471-6698-6

Electronic ISBN: 978-1-4471-6699-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-1-4471-6699-3_4

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