An Adaptive Prequential Learning Framework for Bayesian Network Classifiers

  • Gladys Castillo
  • João Gama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


We introduce an adaptive prequential learning framework for Bayesian Network Classifiers which attempts to handle the cost-performance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Naïve Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute dependencies, and then by searching for new dependences in the extended search space. Since updating the structure is a costly task, we use new data to primarily adapt the parameters and only if this is really necessary, do we adapt the structure. The method for handling concept drift is based on the Shewhart P-Chart. We evaluated our adaptive algorithms on artificial domains and benchmark problems and show its advantages and future applicability in real-world on-line learning systems.


Bayesian Network Concept Change Adaptive Algorithm Concept Drift Concept Shift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brumen, B., Golob, I., Jaakkola, H., Welzer, T., Rozman, I.: Early Assessment of Classification Performance. Australasian CS Week Frontiers, pp. 91–96 (2004)Google Scholar
  2. 2.
    Castillo, G., Gama, J., Medas, P.: Adaptation to Drifting Concepts. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS, vol. 2902, pp. 279–293. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Castillo, G., Gama, J.: Bias Management of Bayesian Network Classifiers. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 70–83. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Castillo, G.: Adaptive Learning Algorithms for Bayesian Network Classifiers. PhD. Dissertation, University of Aveiro (2006)Google Scholar
  5. 5.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning 29, 131–161 (1997)zbMATHCrossRefGoogle Scholar
  6. 6.
    Dawid, A.P.: Statistical theory: The prequential approach. Journal of the Royal Statistical Society A 147, 278–292 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Gama, J.: Iterative Bayes. Theoretical Computer Science 292-2, 417–430 (2003)CrossRefMathSciNetGoogle Scholar
  8. 8.
    O’Rourke, J.O.: Computational Geometry in C. Cambridge University Press, Cambridge (1992)Google Scholar
  9. 9.
    Sahami, M.: Learning Limited Dependence Bayesian Classifiers. In: Proceedings of KDD 1996, vol. 10, pp. 335–338. AAAI Press, Menlo Park (1996)Google Scholar
  10. 10.
    Sen, P.K.: Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association 63, 1379–1389 (1968)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Context. Machine Learning 23, 69–101 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gladys Castillo
    • 1
    • 2
  • João Gama
    • 1
  1. 1.LIACCUniversity of PortoPortugal
  2. 2.Department of MathematicsUniversity of AveiroPortugal

Personalised recommendations