Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

@article{Sun2016EvolutionaryUB,
  title={Evolutionary under-sampling based bagging ensemble method for imbalanced data classification},
  author={Bo Sun and Haiyan Chen and Jiandong Wang and Hua Xie},
  journal={Frontiers of Computer Science},
  year={2016},
  pages={1-20}
}
In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested. To solve this problem, many effective approaches have been proposed. Among them, the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods… CONTINUE READING
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches

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  • IEEE Transactions on Systems, Man, and…
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