Hybrid Sampling with Bagging for Class Imbalance Learning

@inproceedings{Lu2016HybridSW,
  title={Hybrid Sampling with Bagging for Class Imbalance Learning},
  author={Yang Lu and Yiu-ming Cheung and Yuan Yan Tang},
  booktitle={PAKDD},
  year={2016}
}
For class imbalance problem, the integration of sampling and ensemble methods has shown great success among various methods. Nevertheless, as the representatives of sampling methods, undersampling and oversampling cannot outperform each other. That is, undersampling fits some data sets while oversampling fits some other. Besides, the sampling rate also significantly influences the performance of a classifier, while existing methods usually adopt full sampling rate to produce balanced training… CONTINUE READING
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches

  • M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, F. Herrera
  • IEEE Trans. Syst. Man Cybern. Part C Appl. Rev…
  • 2012
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