Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 27, Issue 2, Pages 165-182Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/S0888-613X(01)00039-1
Keywords
Bayesian network; Noisy-OR gate; learning conditional probability distributions
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Existing data sets of cases can significantly reduce the knowledge engineering effort required to parameterize Bayesian networks. Unfortunately, when a data set is small, many conditioning cases are represented by too few or no data records and they do not offer sufficient basis for learning conditional probability distributions, We propose a method that uses Noisy-OR gates to reduce the data requirements in learning conditional probabilities. We test our method on HEPAR II, a model for diagnosis of liver disorders, whose parameters are extracted from a real, small set of patient records. Diagnostic accuracy of the multiple-disorder model enhanced with the Noisy-OR parameters was 6.7% better than the accuracy of the plain multiple-disorder model and 14.3% better than a single-disorder diagnosis model. (C) 2001 Elsevier Science Inc. All rights reserved.
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