querybycommittee
Query by committee (QBC) is an active learning strategy that uses disagreement among a group of models, or a committee, to select the most informative unlabeled examples for labeling. The central idea is that instances on which the committee members disagree are likely to be informative for improving the predictor, thereby reducing labeling effort.
Typically, QBC starts with a small labeled dataset and a larger pool of unlabeled data. A diverse
Key aspects include the composition of the committee (the models can differ in algorithms, training subsets,
QBC was introduced in the early 1990s by Seung, Opper, and Sompolinsky, and has since become a