论文标题
在存在测量误差的情况下改善贝叶斯网络结构学习
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
论文作者
论文摘要
从观察数据中学习贝叶斯网络图的结构学习算法通常是通过假设数据正确反映变量的真实分布来做到这一点。但是，在存在测量误差的情况下，该假设不存在，这可能导致伪边缘。这就是这些算法的合成性能通常高估了现实世界的性能的原因之一。本文描述了一种算法，可以在任何结构学习算法结束时添加为额外的学习阶段，并用作校正学习阶段，可消除潜在的假阳性边缘。结果表明，所提出的校正算法成功地改善了在存在测量误差的情况下跨越不同类别学习的四种完善的结构学习算法的图形分数。
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic performance of these algorithms often overestimates real-world performance. This paper describes an algorithm that can be added as an additional learning phase at the end of any structure learning algorithm, and serves as a correction learning phase that removes potential false positive edges. The results show that the proposed correction algorithm successfully improves the graphical score of four well-established structure learning algorithms spanning different classes of learning in the presence of measurement error.
