  Many active learning methods belong to the retraining-based approaches, which
select one unlabeled instance, add it to the training set with its possible
labels, retrain the classification model, and evaluate the criteria that we
base our selection on. However, since the true label of the selected instance
is unknown, these methods resort to calculating the average-case or worse-case
performance with respect to the unknown label. In this paper, we propose a
different method to solve this problem. In particular, our method aims to make
use of the uncertainty information to enhance the performance of
retraining-based models. We apply our method to two state-of-the-art algorithms
and carry out extensive experiments on a wide variety of real-world datasets.
The results clearly demonstrate the effectiveness of the proposed method and
indicate it can reduce human labeling efforts in many real-life applications.
