Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data

@article{Cieslak2008StartGO,
  title={Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data},
  author={David A. Cieslak and Nitesh V. Chawla},
  journal={2008 Eighth IEEE International Conference on Data Mining},
  year={2008},
  pages={143-152}
}
Class imbalance is a ubiquitous problem in supervised learning and has gained wide-scale attention in the literature. Perhaps the most prevalent solution is to apply sampling to training data in order improve classifier performance. The typical approach will apply uniform levels of sampling globally. However, we believe that data is typically multi-modal, which suggests sampling should be treated locally rather than globally. It is the purpose of this paper to propose a framework which first… CONTINUE READING
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