A Selective Sampling Method for Imbalanced Data Learning on Support Vector Machines

@inproceedings{Choi2015ASS,
  title={A Selective Sampling Method for Imbalanced Data Learning on Support Vector Machines},
  author={Jong Choi},
  year={2015}
}
The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector Machine (SVM) classification algorithm, research efforts have focused on reducing the size of… CONTINUE READING
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