Bounds for Bayesian Network Classifiers with Reduced Precision Parameters

Bounds for Bayesian Network Classifiers with Reduced Precision Parameters

Abstract

Bayesian network classifiers are probabilistic classifiers achieving good classification rates in various applications. These classifiers consist of a directed acyclic graph and a set of conditional probability densities, which in case of discrete-valued nodes can be represented by conditional probability tables. In this paper, we investigate the effect of quantizing these conditional probabilities. We derive worst-case and best-case bounds on the classification rate using interval arithmetic. Furthermore, we determine performance bounds that hold with a user specified confidence using quantization theory. Our results emphasize that only small bit-widths are necessary to achieve good classification rates.

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Authors
  • Tschiatschek, Sebastian
  • Cancino Chacón, Carlos Eduardo
  • Pernkopf, Franz
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Divisions
Data Mining and Machine Learning
Event Location
Vancouver, Canada
Event Type
Conference
Event Dates
26.-31.05.2013
Series Name
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
ISSN/ISBN
978-1-4799-0356-6
Page Range
pp. 3357-3361
Date
26 May 2013
Official URL
https://www.tschiatschek.net/files/tschiatschek13B...
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