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Data and Information Quality"],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Record linkage is the process of identifying records that refer to the same real-world entity within or across databases. If training data in the form of true matches (two records referring to the same entity) and true non-matches (two records referring to different entities) are available, then record linkage can be viewed as a supervised classification problem. Performance measures such as precision, recall, and the F-measure, are commonly used to evaluate the linkage quality obtained with a trained classifier. However, as we show in this article, comparing multiple classifiers using such measures can lead to inconsistent evaluation because for a given measure the same result can be obtained from different classification outcomes. This can cause a suboptimal classifier being selected, which can result in linked data sets of poor quality and possibly wrong decisions being made. To overcome this problem, we propose the\n                    <jats:italic toggle=\"yes\">Consistent Record Linkage<\/jats:italic>\n                    (CRL) measure, an application focused evaluation method that ensures record linkage classifiers are assessed in a fair and transparent way. The CRL-measure allows a user to define the maximum error rates that are acceptable for their linkage application, and it provides practically useful information about the robustness of a classifier with respect to the range of classification thresholds obtained with these error rates. We evaluate the CRL-measure on both synthetic and real-world data sets using multiple classifiers to show its advantage over standard performance measures.\n                  <\/jats:p>","DOI":"10.1145\/3786768","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:16:12Z","timestamp":1767647772000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Consistently Evaluating Record Linkage Classifiers"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3435-2015","authenticated-orcid":false,"given":"Peter","family":"Christen","sequence":"first","affiliation":[{"name":"School of Computing, Australian National University","place":["Canberra, Australia"]},{"name":"Scottish Centre for Administrative Data Research, University of Edinburgh","place":["Canberra, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5844-0111","authenticated-orcid":false,"given":"Sumayya","family":"Ziyad","sequence":"additional","affiliation":[{"name":"School of Computing, Australian National University","place":["Canberra, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7603-1845","authenticated-orcid":false,"given":"Charini","family":"Nanayakkara","sequence":"additional","affiliation":[{"name":"School of Computing, Australian National University","place":["Canberra, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","first-page":"52","article-title":"Neural networks for entity matching: A survey","volume":"15","author":"Barlaug Nils","year":"2021","unstructured":"Nils Barlaug and Jon Atle Gulla. 2021. 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