A self-training approach to cost sensitive uncertainty sampling

  title={A self-training approach to cost sensitive uncertainty sampling},
  author={Alexander Liu and Goo Jun and Joydeep Ghosh},
  journal={Machine Learning},
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
Highly Cited
This paper has 39 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.


Publications citing this paper.
Showing 1-10 of 19 extracted citations

Similar Papers

Loading similar papers…