Based on different sample selection strategies, they can be grouped into two main categories: (1) uncertainty sampling (Lewis and Gale, 2004) where the active learner iteratively select most uncertain unlabeled samples for manual annotation; and (2) committee-based sampling where the active learner selects those unlabeled samples which have the largest disagreement among several committee classifiers.
although the certainty approach could select many MI samples for annotation, this approach performs worst due to its totally ignoring the uncertainty factor.