Learning Bayesian network parameters from small data sets: application of Noisy-OR gates
✍ Scribed by Agnieszka Oniśko; Marek J. Druzdzel; Hanna Wasyluk
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 142 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0888-613X
No coin nor oath required. For personal study only.
✦ Synopsis
Existing data sets of cases can signi®cantly reduce the knowledge engineering eort 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 oer sucient 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 EPAR II 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.