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  • A Novel Bayesian Framework for Online Imbalanced Learning

    Author(s)
    Thi, Thu Thuy Nguyen
    Liew, Alan Wee-Chung
    Tien, Thanh Nguyen
    Wang, Shilin
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Nguyen, Tien Thanh T.
    Nguyen, Thi Thu Thuy
    Year published
    2017
    Metadata
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    Abstract
    We present OCSB, a novel online Bayesian framework for imbalance multi-class data streams. To the best of our knowledge, OCSB is the first online method applying both cost-sensitive learning and sampling technique in a single classifier to deal with class imbalance learning. Specifically, an artificial cost matrix is designed and adapted in a sequential manner to not only boost the accuracy of minority classes but also guarantee stable Gmean - geometric mean of the accuracies for all classes. Furthermore, we introduce a new intermediate random sampling strategy with over- sampled minority classes and under-sampled majority ...
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    We present OCSB, a novel online Bayesian framework for imbalance multi-class data streams. To the best of our knowledge, OCSB is the first online method applying both cost-sensitive learning and sampling technique in a single classifier to deal with class imbalance learning. Specifically, an artificial cost matrix is designed and adapted in a sequential manner to not only boost the accuracy of minority classes but also guarantee stable Gmean - geometric mean of the accuracies for all classes. Furthermore, we introduce a new intermediate random sampling strategy with over- sampled minority classes and under-sampled majority classes. This offers twofold benefit: learning the rare classes properly and reducing the cost caused by the redundant data of majority classes. Experimental results show that our OCSB outperforms very recent well-known methods for online imbalanced learning algorithms in the literature.
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    Conference Title
    2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
    Volume
    2017-December
    DOI
    https://doi.org/10.1109/DICTA.2017.8227393
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/377985
    Collection
    • Conference outputs

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