A note on the F-measure for evaluating record linkage algorithms (and classification methods and information retrieval systems)

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• David Hand (Imperial College London); Peter Christen (Australian National University)
• Thursday 08 September 2016, 15:30-16:30
• Seminar Room 2, Newton Institute.

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DLA - Data linkage and anonymisation

Record linkage is the process of identifying and linking records about the same entities from one more databases. If applied on a single database the process is known as deduplication. Record linkage can be viewed as a classification problem where the aim is to decide if a pair of records is a match (the two records refer to the same real-world entity) or a non-match (the two records refer to two different entities). Various classification techniques – including supervised, unsupervised, semi-supervised and active learning based – have been employed for record linkage. If ground truth data in the form of known true matches and non-matches are available, the quality of classified links can be evaluated. Due to the generally high class imbalance in record linkage problems, standard accuracy or misclassification rate are not meaningful for assessing the quality of a set of linked records. Instead, precision and recall, as commonly used in information retrieval, are used. These are often combined into the popular F-measure, which is normally presented as the harmonic mean of precision and recall. We show that F-measure can be expressed as a weighted sum of precision and recall, with weights which depend on the linkage method being used. This reformulation reveals the measure to have a major conceptual weakness: the relative importance assigned to precision and recall should be an aspect of the problem and the user, but not of the particular instrument being used. We suggest alternative measures which do not suffer from this fundamental flaw.

This talk is part of the Isaac Newton Institute Seminar Series series.

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