  Active learning (AL) prioritizes the labeling of the most informative data
samples. However, the performance of AL heuristics depends on the structure of
the underlying classifier model and the data. We propose an imitation learning
scheme that imitates the selection of the best expert heuristic at each stage
of the AL cycle in a batch-mode pool-based setting. We use DAGGER to train the
policy on a dataset and later apply it to datasets from similar domains. With
multiple AL heuristics as experts, the policy is able to reflect the choices of
the best AL heuristics given the current state of the AL process. Our
experiment on well-known datasets show that we both outperform state of the art
imitation learners and heuristics.
