A self-training approach to cost sensitive uncertainty sampling

@article{Liu2009ASA,
  title={A self-training approach to cost sensitive uncertainty sampling},
  author={Alexander Liu and Goo Jun and Joydeep Ghosh},
  journal={Machine Learning},
  year={2009},
  volume={76},
  pages={257-270}
}
Uncertainty sampling is an effective method for performing active learning that is computationally efficient compared to other active learning methods such as loss-reduction methods. However, unlike loss-reduction methods, uncertainty sampling cannot minimize total misclassification costs when errors incur different costs. This paper introduces a method for performing cost-sensitive uncertainty sampling that makes use of self-training. We show that, even when misclassification costs are equal… CONTINUE READING
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