Learning Bayesian network parameters from small data sets: application of Noisy-OR gates

Existing data sets of cases can significantly reduce the knowledge engineer ing 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 conditi onal probability distributions, We propose a method that uses Noisy-OR gate s to reduce the data requirements in learning conditional probabilities. We test our method on HEPAR II, a model for diagnosis of liver disorders, who se parameters are extracted from a real, small set of patient records. Diag nostic accuracy of the multiple-disorder model enhanced with the Noisy-OR p arameters 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 Els evier Science Inc. All rights reserved.