Batch-Mode Active Learning via Error Bound Minimization

Abstract

Active learning has been proven to be quite effec-tive in reducing the human labeling efforts by ac-tively selecting the most informative examples to label. In this paper, we present a batch-mode ac-tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo-gistic regression conditioned on any fixed train-ing sample. It is different from a typical PAC-style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme-diately used to design an active learning algo-rithm by minimizing this bound iteratively. We also discuss the connections between the pro-posed method and some existing active learn-ing approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly. 1