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    Learning in imbalanced relational data

    Access Status
    Fulltext not available
    Authors
    Ghanem, Amal
    Venkatesh, Svetha
    West, Geoff
    Date
    2008
    Type
    Conference Paper
    
    Metadata
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    Citation
    Ghanem, A. and Venkatesh, S. and West, G. 2008. Learning in imbalanced relational data, in Ejiri, M. and Kasturi, R. and Sanniti di Baja, G. (ed), 19th international Conference on Pattern Recognition, Dec 8-11 2008. Tampa, Florida: IAPR.
    Source Title
    Proceedings of the 19th international conference on Pattern Recognition
    Source Conference
    19th international conference on Pattern Recognition
    DOI
    10.1109/ICPR.2008.4761095
    ISBN
    9781424421756
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/2826
    Collection
    • Curtin Research Publications
    Abstract

    Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes. We propose to extend a relational learning technique called Probabilistic Relational Models (PRMs) to deal with the imbalanced class problem. We address learning from imbalanced relational data using an ensemble of PRMs and propose a new model: the PRMs-IM. We show the performance of PRMs-IM on a real university relational database to identify students at risk.

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