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The common uncertain sampling approach searches for the most uncertain samples closest to the decision boundary for a classification task. However, we might fail to find the uncertain samples when we have a poor probabilistic model. In this work, we develop an active learning strategy called "Uncertainty Sampling with Biasing Consensus" (USBC) which predicts the unbalanced data by multi-model committee and ranks the informativeness of samples by uncertainty sampling with higher weight on thedblp:journals/jmlr/ChenM11 fatcat:ufkim6golnfajle7ykd6cdkny4