A Bayesian network based learning system: — Architecture and performance comparison with other methods

  • Kazuo J. Ezawa
  • Til Schuermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 946)


In this paper, we discuss the construction of Bayesian network models from data using the Advanced Pattern Recognition & Identification (APRI) system. It is designed for classification of low probability events as well as mixed data types, discrete and continuous, with large amounts of available training data (a few million records for a typical application) where other methods such as discriminant analysis and classification trees have difficulty in doing the task. We show here that APRI does as well and in some cases better then these other methods with less demanding problems. We will discuss the architecture of the system as an example of Bayesian network learning system. We present a comparison of this system with the classification tree system C4.5 and statistical discriminant analysis using standard data sets, namely voting record and CRX credit card application. We show that despite the fact that APRI was not designed for small data set applications, it nevertheless performs well. We discuss functional advantages and disadvantages between classification tree (C4.5) and Bayesian network (APRI) methods.


Bayesian Classification Bayesian Learning Bayesian Networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Kazuo J. Ezawa
    • 1
  • Til Schuermann
    • 2
  1. 1.AT&T Bell LaboratoriesMurray Hill
  2. 2.AT&T Bell LaboratoriesMurray Hill

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