Surprise sampling: Improving and extending the local case-control sampling

  title={Surprise sampling: Improving and extending the local case-control sampling},
  author={Xinwei Shen and Kani Chen and Wen Yu},
  journal={arXiv: Methodology},
Fithian and Hastie (2014) proposed a new sampling scheme called local case-control (LCC) sampling that achieves stability and efficiency by utilizing a clever adjustment pertained to the logistic model. It is particularly useful for classification with large and imbalanced data. This paper proposes a more general sampling scheme based on a working principle that data points deserve higher sampling probability if they contain more information or appear "surprising" in the sense of, for example… 

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