  Active learning aims to reduce annotation cost by predicting which samples
are useful for a human expert to label. Although this field is quite old,
several important challenges to using active learning in real-world settings
still remain unsolved. In particular, most selection strategies are
hand-designed, and it has become clear that there is no best active learning
strategy that consistently outperforms all others in all applications. This has
motivated research into meta-learning algorithms for "learning how to actively
learn". In this paper, we compare this kind of approach with the association of
a Random Forest with the margin sampling strategy, reported in recent
comparative studies as a very competitive heuristic. To this end, we present
the results of a benchmark performed on 20 datasets that compares a strategy
learned using a recent meta-learning algorithm with margin sampling. We also
present some lessons learned and open future perspectives.
