Improving Temporal Record Linkage Using Regression Classification
Improving Temporal Record Linkage Using Regression Classification
复制标题
使用回归分类改进时间记录链接
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
P. Christen
中科院分区：
文献类型：
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作者：
Yichen Hu;Qing Wang;Dinusha Vatsalan;P. Christen
时间记录链接是识别在一段时间内收集的记录组的过程，例如在人口普查或选民登记数据库中，其中同一组中的记录代表相同的现实世界实体。此类数据库通常包含时间信息，例如创建记录或修改记录的时间。与传统的记录链接不同，传统的记录链接将同一实体的记录之间的差异视为错误或变化，时间记录链接旨在从已知属性值随时间变化的实体捕获记录。在本文中，我们提出了一种新颖的方法，它扩展了称为衰减模型的现有时间方法，以分类计算每个属性的变化概率。我们的新颖方法使用基于回归的机器学习模型来预测属性集的衰减。每个这样的属性集都具有原则属性和支持属性，其中支持属性的值可以影响原则属性的衰减。我们在真实的美国选民数据库上的实验结果表明，与衰减模型方法相比，我们提出的方法可以产生更好的链接质量。
Temporal record linkage is the process of identifying groups of records that are collected over a period of time, such as in census or voter registration databases, where records in the same group represent the same real-world entity. Such databases often contain temporal information, such as the time when a record was created or when it was modified. Unlike traditional record linkage, which considers differences between records from the same entity as errors or variations, temporal record linkage aims to capture records from entities where the attribute values are known to change over time. In this paper we propose a novel approach that extends an existing temporal approach called decay model, to categorically calculate probabilities of change for each attribute. Our novel method uses a regression-based machine learning model to predict decays for sets of attributes. Each such set of attributes has a principle attribute and support attributes, where values of the support attributes can affect the decay of the principle attribute. Our experimental results on a real US voter database show that our proposed approach results in better linkage quality compared to the decay model approach.