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dc.contributor.authorFu, Z
dc.contributor.authorZhou, J
dc.contributor.authorChristen, P
dc.contributor.authorBoot, M
dc.contributor.editorPang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey
dc.date.accessioned2017-08-31T02:11:40Z
dc.date.available2017-08-31T02:11:40Z
dc.date.issued2012
dc.date.modified2013-01-04T00:13:26Z
dc.identifier.isbn9783642302169
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-642-30217-6_15
dc.identifier.urihttp://hdl.handle.net/10072/48869
dc.description.abstractRecord linkage is the process of identifying records that refer to the same entities from different data sources. While most research efforts are concerned with linking individual records, new approaches have recently been proposed to link groups of records across databases. Group record linkage aims to determine if two groups of records in two databases refer to the same entity or not. One application where group record linkage is of high importance is the linking of census data that contain household information across time. In this paper we propose a novel method to group record linkage based on multiple instance learning. Our method treats group links as bags and individual record links as instances. We extend multiple instance learning from bag to instance classification to reconstruct bags from candidate instances. The classified bag and instance samples lead to a significant reduction in multiple group links, thereby improving the overall quality of linked data. We evaluate our method with both synthetic data and real historical census data.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenamePAKDD 2012
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2012-05-29
dc.relation.ispartofdateto2012-06-01
dc.relation.ispartoflocationKuala Lumpur, Malaysia
dc.relation.ispartofpagefrom171
dc.relation.ispartofpageto182
dc.relation.ispartofissuePART 1
dc.relation.ispartofvolume7301 LNAI
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleMultiple Instance Learning for Group Record Linkage
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2012 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

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