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  • A Bag Reconstruction Method for Multiple Instance Classification and Group Record Linkage

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    Author(s)
    Fu, Z
    Zhou, J
    Peng, F
    Christen, P
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2012
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    Abstract
    Record linking is the task of detecting records in several databases that refer to the same entity. This task aims at exploring the relationship between entities, which normally lack common identifiers in heterogeneous datasets. When entities contain multiple relational records, linking them across datasets can be more accurate by treating the records as groups, which leads to group linking methods. Even so, individual record links may still be needed for the final group linking step. This problem can be solved by multiple instance learning, in which group links are modelled as bags, and record links are considered as ...
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    Record linking is the task of detecting records in several databases that refer to the same entity. This task aims at exploring the relationship between entities, which normally lack common identifiers in heterogeneous datasets. When entities contain multiple relational records, linking them across datasets can be more accurate by treating the records as groups, which leads to group linking methods. Even so, individual record links may still be needed for the final group linking step. This problem can be solved by multiple instance learning, in which group links are modelled as bags, and record links are considered as instances. In this paper, we propose a novel method for instance classification and group record linkage via bag reconstruction from instances. The bag reconstruction is based on the modeling of the distribution of negative instances in the training bags via kernel density estimation. We evaluate this approach on both synthetic and real-world data. Our results show that the proposed method can outperform several baseline methods.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    7713 LNAI
    DOI
    https://doi.org/10.1007/978-3-642-35527-1_21
    Copyright Statement
    © 2012 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/52240
    Collection
    • Conference outputs

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