Active Learning to Maximize Area Under the ROC Curve

Abstract

In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active… (More)
DOI: 10.1109/ICDM.2006.12

Topics

7 Figures and Tables