A Novel Bayesian Framework for Online Imbalanced Learning

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 artific...

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Bibliographic Details
Published in2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) pp. 1 - 7
Main Authors Nguyen, Thi Thu Thuy, Liew, Alan Wee-Chung, Nguyen, Tien Thanh, Wang, Shilin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
Bayes methods
Classification algorithms
Learning systems
Prediction algorithms
Sampling methods
Training
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DOI10.1109/DICTA.2017.8227393

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Summary: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.
DOI:10.1109/DICTA.2017.8227393