Regression classification for Improved Temporal Record Linkage

dc.contributor.authorWang, Qing
dc.contributor.authorVatsalan, Dinusha
dc.contributor.authorChristen, Peter
dc.contributor.authorHu, Yichen
dc.contributor.editorEstivill-Castro, V.
dc.contributor.editorSimoff, S.
dc.coverage.spatialCanberra, Australia
dc.date.accessioned2024-02-20T00:21:12Z
dc.date.createdDecember 6-8 2016
dc.date.issued2016
dc.date.updated2022-10-02T07:20:20Z
dc.description.abstractTemporal record linkage is the process of identifying groups of records which are collected over long periods of time, such as census databases or voter registration databases, that represent the same real-world entities. These datasets often contain temporal information for each record, such as the time when a record was created, or the time when it was modified. Unlike traditional record linkage, which treats differences between records from the same entity as errors or variations, temporal record linkage aims to capture records from entities where the details of these entities change over the time. This paper proposes a temporal record linkage approach that learns the probabilities for attribute values of records to change within different periods of time, which extends an existing temporal approach decay model. The proposed method uses a regression based machine learning model to predict decay with sets of attributes, where attribute values in each set could affect the decay of others. Our experimental results show that the proposed approach results in generally better recall than baseline approaches on real-world datasets.en_AU
dc.description.sponsorshipThis work was partially funded by the Australian Research Council (ARC) under Discovery Project DP160101934.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.urihttp://hdl.handle.net/1885/313756
dc.language.isoen_AUen_AU
dc.publisherAustralasian Data Mining Conferenceen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP160101934en_AU
dc.relation.ispartofseriesAustralasian Data Mining Conference (AusDM 2016)en_AU
dc.rightsCopyright © 2016, Australian Computer Society, Inc.en_AU
dc.sourceConferences in Research and Practice in Information Technologyen_AU
dc.subjectData matchingen_AU
dc.subjectentity resolutionen_AU
dc.subjectrecord linkageen_AU
dc.subjecttemporal dataen_AU
dc.titleRegression classification for Improved Temporal Record Linkageen_AU
dc.typeConference paperen_AU
local.bibliographicCitation.lastpage10en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationWang, Qing, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationVatsalan, Dinusha, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationChristen, Peter, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationHu, Yichen, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoremailu4021539@anu.edu.auen_AU
local.contributor.authoruidWang, Qing, u5170295en_AU
local.contributor.authoruidVatsalan, Dinusha, u4908149en_AU
local.contributor.authoruidChristen, Peter, u4021539en_AU
local.contributor.authoruidHu, Yichen, u5986120en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor460507 - Information extraction and fusionen_AU
local.identifier.absfor460504 - Data qualityen_AU
local.identifier.absfor460502 - Data mining and knowledge discoveryen_AU
local.identifier.ariespublicationu6048437xPUB390en_AU
local.identifier.uidSubmittedByu6048437en_AU
local.publisher.urlhttps://ausdm.org/archive/ausdm16/index.htmlen_AU
local.type.statusPublished Versionen_AU

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