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- W2776736331 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 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." @default.
- W2776736331 created "2018-01-05" @default.
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- W2776736331 date "2017-11-01" @default.
- W2776736331 modified "2023-09-23" @default.
- W2776736331 title "A Novel Bayesian Framework for Online Imbalanced Learning" @default.
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- W2776736331 doi "https://doi.org/10.1109/dicta.2017.8227393" @default.
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