论文标题
学习贝叶斯网络，可以充分传播证据
Learning Bayesian Networks that enable full propagation of evidence
论文作者
论文摘要
本文基于贝叶斯网络（BN）结构学习的最新发展，这是在有争议的假设中，即输入变量取决于依赖。该假设可以看作是针对已知或假定为依赖的输入变量的案例的学习约束。它解决了学习多个不能够完全传播证据的多个不相交子图的问题。如果相对于模型的维度较低，那么在使用真实数据时通常是这种情况，此问题非常普遍。本文提出了一种新型的混合结构学习算法，即Saiyanh，该算法解决了这个问题。结果表明，与最先进的算法相比，该约束有助于算法以更高的准确性估算真实边缘的数量。在研究的13个算法中，结果在重建真实dag方面排名第4位，准确性得分降低了8.1％（F1），10.2％（BSF）和19.5％（SHD），而最高排名的算法则降低了19.5％（SHD），而排名最高75.5％（F1），118％（f1），BSF和4.3％（BSF）和4.3％（4.3％）（4.3％）（4.3％）（4.3％）（4.3％（SH）。总体而言，结果表明，在其他算法产生多个脱节子图的情况下，提出的算法会发现令人满意的准确连接的DAG，而这些算法通常经常低于真实图。
This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disjoint subgraphs that do not enable full propagation of evidence. This problem is highly prevalent in cases where the sample size of the input data is low with respect to the dimensionality of the model, which is often the case when working with real data. The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue. The results show that this constraint helps the algorithm to estimate the number of true edges with higher accuracy compared to the state-of-the-art. Out of the 13 algorithms investigated, the results rank SaiyanH 4th in reconstructing the true DAG, with accuracy scores lower by 8.1% (F1), 10.2% (BSF), and 19.5% (SHD) compared to the top ranked algorithm, and higher by 75.5% (F1), 118% (BSF), and 4.3% (SHD) compared to the bottom ranked algorithm. Overall, the results suggest that the proposed algorithm discovers satisfactorily accurate connected DAGs in cases where other algorithms produce multiple disjoint subgraphs that often underfit the true graph.
