SMOTE: Synthetic Minority Over-sampling Technique

@article{Chawla2002SMOTESM,
  title={SMOTE: Synthetic Minority Over-sampling Technique},
  author={N. Chawla and K. Bowyer and Lawrence O. Hall and W. Philip Kegelmeyer},
  journal={ArXiv},
  year={2002},
  volume={abs/1106.1813}
}
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under… 

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