Positive-Unlabeled Learning in the Face of Labeling Bias

@article{Youngs2015PositiveUnlabeledLI,
  title={Positive-Unlabeled Learning in the Face of Labeling Bias},
  author={Noah Youngs and Dennis Shasha and Richard Bonneau},
  journal={2015 IEEE International Conference on Data Mining Workshop (ICDMW)},
  year={2015},
  pages={639-645}
}
Positive-Unlabeled (PU) learning scenarios are a class of semi-supervised learning where only a fraction of the data is labeled, and all available labels are positive. The goal is to assign correct (positive and negative) labels to as much data as possible. Several important learning problems fall into the PU-learning domain, as in many cases the cost and feasibility of obtaining negative examples is prohibitive. In addition to the positive-negative disparity the overall cost of labeling these… CONTINUE READING

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