Learning Bayesian-Neural Network from Mixed-Mode Data

  • Wang Limin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


For reasoning with uncertain knowledge the use of probability theory has been broadly investigated. This paper proposed a novel probabilistic network named Bayesian-Neural Network (BNN). BNN reduces computational complexity by dividing input attribute set into two parts, each modelled by Bayesian network or Neural network. The outputs produced by different classifiers is then solved in the output space by estimating the class-conditional structural mixtures. Empirical studies on a set of natural domains show that BNN has clear advantages with respect to the generalization ability.


Bayesian Network Continuous Attribute Probabilistic Neural Network Attribute Node Probabilistic Network 
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.


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  1. 1.
    Lin, W.M., Lin, C.H., Tasy, M.X.: Transformer-fault diagnosis by integrating field data and standard codes with training enhancible adaptive probabilistic network. IEE Proceedings of Generation, Transmission and Distribution 152, 335–341 (2005)CrossRefGoogle Scholar
  2. 2.
    Tseng, C.L., Chen, Y.H., Xu, Y.Y., Pao, H.T., Fu, H.-C.: A self-growing probabilistic decision-based neural network with automatic data clustering. Neurocomputing 61, 21–38 (2004)CrossRefGoogle Scholar
  3. 3.
    Malgorzata, S.A.S.: Probabilistic fault localization in communication systems using belief networks Steinder. IEEE/ACM Transactions on Networking 12, 809–822 (2004)CrossRefGoogle Scholar
  4. 4.
    Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)CrossRefGoogle Scholar
  5. 5.
    Kononenko, I.: Semi-naive Bayesian classifier. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 206–219. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  6. 6.
    Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proceedings of AAAI 1992, vol. 92, pp. 223–228 (1992)Google Scholar
  7. 7.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning 29, 131–163 (1997)MATHCrossRefGoogle Scholar
  8. 8.
    Pazzani, M.J., Keogh, E.J.: Learning Augmented Bayesian Classifiers: A Comparison of Distribution-Based and Classification-Based Approaches. In: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, pp. 225–230 (1999)Google Scholar
  9. 9.
    Hendler, J.: Developing hybrid symbolic/connectionist models. In: Advances in Connectionist and Neural Computation Theory, pp. 165–179 (1991)Google Scholar
  10. 10.
    Sun, R., Soolanan, S.L.A.: Working Notes of the AAAI Workshop on Integrating Neural and Symbolic Processes, pp. 205–217 (1992)Google Scholar
  11. 11.
    Friedman, N., Goldszmidt, M., Thomas, J.L.: Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. In: Proceedings of the International Conference on Machine Learning, pp. 179–187 (1998)Google Scholar
  12. 12.
    Dougherty, J.: Supervised and unsupervied discretization of coninuous features. In: Proceedings of the 12th International Conference on Machine Learning, pp. 194–201 (1995)Google Scholar
  13. 13.
    Chow, C.K., Liu, C.N.: Approximating Discrete Probability Distributions with Dependence Trees. IEEE Transactions on Information Theory 14, 462–467 (1968)MATHCrossRefGoogle Scholar
  14. 14.
    Hunter, A.: Feature Selection Using Probabilistic Neural Networks. Neural Computing and Applications 9, 124–132 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wang Limin
    • 1
  1. 1.Key Laboratary of Symbol Computation and Knowledge Engineering of Ministry, of EducationJiLin UniversityChangChunChina

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