Bayesian Classifiers

  • Luis Enrique SucarEmail author
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


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.


  1. 1.
    Bache, K., Lichman, M.: UCI machine learning repository. University of California, School of Information and Computer Science. Irvine. Accessed 22 Sept 2014 (2013)
  2. 2.
    Bielza, C., Li, G., Larrañaga, P.: Multi-dimensional classification with bayesian networks. Int. J. Approx. Reason. 52, 705–727 (2011)zbMATHCrossRefGoogle Scholar
  3. 3.
    Borchani, H., Bielza, C., Toro, C., Larrañaga, P.: Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artif. Intell. Med. 57, 219–229 (2013)CrossRefGoogle Scholar
  4. 4.
    Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 101–108 (1999)Google Scholar
  5. 5.
    Drummond, C., Holte, R.C.: Explicitly representing expected cost: an alternative to the ROC representation. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 198–207 (2000)Google Scholar
  6. 6.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)zbMATHCrossRefGoogle Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B. and Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. In: ACM SIGKDD Explorations Newsletter. ACM, pp. 10–18 (2009)Google Scholar
  8. 8.
    Kwoh, C.K., Gillies, D.F.: Using hidden nodes in Bayesian networks. Artificial Intelligence, vol. 88, pp. 1–38. Elsevier, Essex (1996)Google Scholar
  9. 9.
    Martinez, M., Sucar, L.E.: Learning an optimal naive Bayes classifier. In: International Conference on Pattern Recognition (ICPR), vol. 3, pp. 1236–1239 (2006)Google Scholar
  10. 10.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Howard, England (2004)Google Scholar
  11. 11.
    Pazzani, M.J.: Searching for Dependencies in Bayesian Classifiers. Artificial Intelligence and Statistics IV. Lecture Notes in Statistics, Springer-Verlag, New York (1997)Google Scholar
  12. 12.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Proceedings ECML/PKDD, pp. 254–269 (2009)Google Scholar
  13. 13.
    Ramírez, M., Sucar, L.E., Morales, E.: Path evaluation for hierarchical multi-label classification. In: Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 502–507 (2014)Google Scholar
  14. 14.
    Silla Jr., C.N., Freitas, A.A.: Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3499–3504. October 2009Google Scholar
  15. 15.
    Silla Jr, C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22(1–2), 31–72 (2011)zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Sucar, L.E., Gillies, D.F., Gillies, D.A.: Objective probabilities in expert systems. Artif. Intell. 61, 187–208 (1993)MathSciNetCrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Wareh. Min. 3, 1–13 (2007)CrossRefGoogle Scholar
  19. 19.
    van der Gaag L.C., de Waal, P.R.: Multi-dimensional Bayesian network classifiers. In: Third European Conference on Probabilistic Graphic Models, pp. 107–114. Prague, Czech Republic (2006)Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Santa María TonantzintlaMexico

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