| dc.contributor.author | Lee, Danhyang | |
| dc.contributor.author | Zhang, Li-Chun | |
| dc.contributor.author | Kim, Jae Kwang | |
| dc.date.accessioned | 2023-03-01T18:19:50Z | |
| dc.date.available | 2023-03-01T18:19:50Z | |
| dc.date.created | 2023-02-21T18:01:10Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Survey Methodology. 2022, 48 (1), 1-23. | |
| dc.identifier.issn | 0714-0045 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3055140 | |
| dc.description.abstract | By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in machine learning to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases. | |
| dc.description.abstract | Maximum entropy classification for record linkage | |
| dc.language.iso | eng | |
| dc.relation.uri | http://www.statcan.gc.ca/pub/12-001-x/2022001/article/00007-eng.htm. | |
| dc.title | Maximum entropy classification for record linkage | |
| dc.title.alternative | Maximum entropy classification for record linkage | |
| dc.type | Peer reviewed | |
| dc.type | Journal article | |
| dc.description.version | publishedVersion | |
| dc.source.pagenumber | 1-23 | |
| dc.source.volume | 48 | |
| dc.source.journal | Survey Methodology | |
| dc.source.issue | 1 | |
| dc.identifier.cristin | 2127977 | |
| cristin.ispublished | true | |
| cristin.fulltext | original | |
| cristin.qualitycode | 1 | |
