The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose an efficient greedy algorithm that is based on the property of submodular functions. Our empirical studies with five UCI datasets and one realworld medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-ofthe-art algorithms for active learning.
REF proposes a framework that minimizes the Fisher information and solves this optimization problem using the submodular properties of the set selection function.
Batch mode active learning and its application to medical image classification
ICML '06
Computer Science