Ensemble Learning combines multiple learned models under the assumption that "two (or more) heads are better than one." The decisions of multiple hypotheses are combined in ensemble learning to produce more accurate results. Boosting and bagging are two popular approaches. Our work focuses on building diverse committees that are more effective than those built by existing methods, and, in particular, are useful for active learning.

For a general, popular book on the utility of combining diverse, independent opinions in human decision-making, see The Wisdom of Crowds.