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    Multi-class Pattern Classification in Imbalanced Data

    154502_154502.pdf (205.4Kb)
    Access Status
    Open access
    Authors
    Ghanem, Amal
    Venkatesh, Svetha
    West, Geoffrey
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Ghanem, A.S. and Venkatesh, S. and West, G. 2010. Multi-class Pattern Classification in Imbalanced Data, 2010 International Conference on Pattern Recognition, Aug 23 2010, pp. 2881-2884. Istanbul, Turkey: IEEE.
    Source Title
    Proceedings of 2010 Intenational Conference on Pattern Recognition
    Source Conference
    2010 International Conference on Pattern Recognition
    DOI
    10.1109/ICPR.2010.706
    ISSN
    10514651
    School
    Department of Computing
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/24624
    Collection
    • Curtin Research Publications
    Abstract

    The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.

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