Learning Bayesian network parameters from small data sets: application of Noisy-OR gates
Learning Bayesian network parameters from small data sets: application of Noisy-OR gates
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DOI：
10.1016/s0888-613x(01)00039-1
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发表时间：
2001-08-01
影响因子：
3.9
通讯作者：
Wasyluk, H
中科院分区：
文献类型：
--
作者：
Onisko, A;Druzdzel, MJ;Wasyluk, H
现有的数据集的情况下，可以显着减少所需的知识工程的努力，参数化贝叶斯网络。不幸的是，当数据集很小时，许多条件情况都由太少或没有数据记录来表示，并且它们无法为学习条件概率分布提供足够的基础。我们提出了一种使用Noisy-OR门来减少数据需求的方法学习条件概率。我们在HEPAR II上测试我们的方法，HEPAR II是一种用于诊断肝脏疾病的模型，其参数是从一组真实的患者记录中提取的。用Noisy-OR参数增强的多疾病模型的诊断准确性比普通多疾病模型的准确性高6.7%，比单疾病诊断模型高14.3%。(C)2001 Elsevier Science Inc. All rights reserved.
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.