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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 74–89Cite as

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Bayesian Network Classifiers with Reduced Precision Parameters

Bayesian Network Classifiers with Reduced Precision Parameters

  • Sebastian Tschiatschek20,
  • Peter Reinprecht20,
  • Manfred Mücke21,22 &
  • …
  • Franz Pernkopf20 
  • Conference paper
  • 4497 Accesses

  • 9 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7523)

Abstract

Bayesian network classifiers (BNCs) are probabilistic classifiers showing good performance in many applications. They consist of a directed acyclic graph and a set of conditional probabilities associated with the nodes of the graph. These conditional probabilities are also referred to as parameters of the BNCs. According to common belief, these classifiers are insensitive to deviations of the conditional probabilities under certain conditions. The first condition is that these probabilities are not too extreme, i.e. not too close to 0 or 1. The second is that the posterior over the classes is significantly different. In this paper, we investigate the effect of precision reduction of the parameters on the classification performance of BNCs. The probabilities are either determined generatively or discriminatively. Discriminative probabilities are typically more extreme. However, our results indicate that BNCs with discriminatively optimized parameters are almost as robust to precision reduction as BNCs with generatively optimized parameters. Furthermore, even large precision reduction does not decrease classification performance significantly. Our results allow the implementation of BNCs with less computational complexity. This supports application in embedded systems using floating-point numbers with small bit-width. Reduced bit-widths further enable to represent BNCs in the integer domain while maintaining the classification performance.

Keywords

  • Bayesian Network Classifiers
  • Custom-precision Analysis
  • Discriminative Classifiers

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Author information

Authors and Affiliations

  1. Signal Processing and Speech Communication Laboratory, Graz University of Technology, Graz, Austria

    Sebastian Tschiatschek, Peter Reinprecht & Franz Pernkopf

  2. Research Group Theory and Applications of Algorithms, University of Vienna, Vienna, Austria

    Manfred Mücke

  3. Sustainable Computing Research, Austria

    Manfred Mücke

Authors
  1. Sebastian Tschiatschek
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  2. Peter Reinprecht
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  3. Manfred Mücke
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  4. Franz Pernkopf
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Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach, Tijl De Bie & Nello Cristianini,  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Tschiatschek, S., Reinprecht, P., Mücke, M., Pernkopf, F. (2012). Bayesian Network Classifiers with Reduced Precision Parameters. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-33460-3_10

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  • Print ISBN: 978-3-642-33459-7

  • Online ISBN: 978-3-642-33460-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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