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    Classifying Multiple imbalanced attributes in relational data

    133772_133772.pdf (177.8Kb)
    Access Status
    Open access
    Authors
    Ghanem, Amal
    Venkatesh, Svetha
    West, Geoffrey
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Ghanem, Amal and Venkatesh, Svetha and West, Geoffrey. 2009. Classifying Multiple imbalanced attributes in relational data, in Ann Nicholson and Xiaodong Li (ed), AI 2009, Dec 1 2009, pp. 220-229. Melbourne: Springer Berlin / Heidelberg.
    Source Title
    AI 2009:Advance in artificial intelligence 22nd australasion joint conference
    Source Conference
    AI 2009
    DOI
    10.1007/978-3-642-10439-8_23
    ISSN
    03029743
    Faculty
    School of Science and Computing
    Department of Computing
    Faculty of Science and Engineering
    URI
    http://hdl.handle.net/20.500.11937/6513
    Collection
    • Curtin Research Publications
    Abstract

    Real-world data are often stored as relational database systems with different numbers of significant attributes. Unfortunately, most classification techniques are proposed for learning from balanced nonrelational data and mainly for classifying one single attribute. In this paper, we propose an approach for learning from relational data withthe specific goal of classifying multiple imbalanced attributes. In our approach, we extend a relational modelling technique (PRMs-IM) designed for imbalanced relational learning to deal with multiple imbalanced attributes classification. We address the problem of classifying multiple imbalanced attributes by enriching the PRMs-IM with the 'Bagging' classification ensemble. We evaluate our approach on real-world imbalanced student relational data and demonstrate its effectiveness in predicting student performance.

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