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Heuristic Supervised Approach for Record Linkage

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7647))

Abstract

Record linkage is a well known technique used to link records from one database to records from another database which make reference to the same individuals. Although it is usually used in database integration, it is also used in the data privacy field for the disclosure risk evaluation of protected datasets. In this paper we compare two different supervised algorithms which rely on distance-based record linkage techniques, specifically using the Choquet integral’s fuzzy integral to compute the distance between records. The first approach uses a linear optimization problem which determines the optimal fuzzy measure for the linkage. While, the second approach is a kind of gradient algorithm with constraints for the fuzzy measures’ identification. We show the advantages and drawbacks of both algorithms and also in which situations they will work better.

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Author information

Authors and Affiliations

  1. CIFASIS-CONICET, Universidad Nacional de Rosario, Argentina

    Javier Murillo

  2. Universitat Autònoma de Barcelona (UAB), Barcelona, Spain

    Daniel Abril

  3. Institut d’Investigació en Intel·ligència Artificial(IIIA), Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, Spain

    Daniel Abril & Vicenç Torra

Authors
  1. Javier Murillo
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  2. Daniel Abril
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  3. Vicenç Torra
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Editor information

Editors and Affiliations

  1. IIIA-CSIC, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10, Naka, 186-0004, Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Universitat de Girona, Campus Montilivi, building EPS-4, 17071, Girona, Spain

    Beatriz López  & Mateu Villaret  & 

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Murillo, J., Abril, D., Torra, V. (2012). Heuristic Supervised Approach for Record Linkage. In: Torra, V., Narukawa, Y., López, B., Villaret, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2012. Lecture Notes in Computer Science(), vol 7647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34620-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-34620-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34619-4

  • Online ISBN: 978-3-642-34620-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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