HNet：图形超几何网络
HNet: Graphical Hypergeometric Networks
摘要：
动机：真实世界的数据通常包含连续和离散值的测量。尽管有许多库可用，但具有混合数据类型的数据集需要密集的预处理步骤，并且描述变量之间的关系仍然是一个挑战。数据理解阶段是数据采掘过程中的重要步骤，然而，在不对数据进行任何假设的情况下，搜索空间的变量数量是超指数的。研究方法：我们提出了图形超几何网络（HNet），这是一种使用统计推断来测试变量之间的显著性关联的方法。其目的是只使用重要的关联来确定网络，以便揭示变量之间的复杂关系。HNet处理原始非结构化数据集并输出由节点之间的（部分）有向或无向边组成的网络（即，变量）。为了评估HNet的准确性，我们使用了众所周知的数据集，此外还生成了具有已知地面实况的数据集。HNet的性能进行了比较贝叶斯结构学习。结果：我们表明，HNet在检测节点链接方面表现出较高的准确性和性能。在警报数据集的情况下，我们可以证明平均MCC得分为0.33 + 0.0002（P<1x 10 -6），而贝叶斯结构学习导致平均MCC得分为0.52 + 0.006（P<1x 10 -11），随机分配边缘导致MCC得分为0.004 + 0.0003（P=0.49）。结论：HNet可以处理原始的非结构化数据集，允许分析混合的数据类型，它很容易扩展变量的数量，并允许详细检查检测到的关联。可用性：https://erdogant.github.io/hnet/
英文摘要：
Motivation: Real-world data often contain measurements with both continuous and discrete values. Despite the availability of many libraries, data sets with mixed data types require intensive pre-processing steps, and it remains a challenge to describe the relationships between variables. The data understanding phase is an important step in the data mining process, however, without making any assumptions on the data, the search space is super-exponential in the number of variables. Methods: We propose graphical hypergeometric networks (HNet), a method to test associations across variables for significance using statistical inference. The aim is to determine a network using only the significant associations in order to shed light on the complex relationships across variables. HNet processes raw unstructured data sets and outputs a network that consists of (partially) directed or undirected edges between the nodes (i.e., variables). To evaluate the accuracy of HNet, we used well known data sets and in addition generated data sets with known ground truth. The performance of HNet is compared to Bayesian structure learning. Results: We demonstrate that HNet showed high accuracy and performance in the detection of node links. In the case of the Alarm data set we can demonstrate on average an MCC score of 0.33 + 0.0002 (P<1x10-6), whereas Bayesian structure learning resulted in an average MCC score of 0.52 + 0.006 (P<1x10-11), and randomly assigning edges resulted in a MCC score of 0.004 + 0.0003 (P=0.49). Conclusions: HNet can process raw unstructured data sets, allows analysis of mixed data types, it easily scales up in number of variables, and allows detailed examination of the detected associations. Availability: https://erdogant.github.io/hnet/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
