  Motivated by applications in protein function prediction, we consider a
challenging supervised classification setting in which positive labels are
scarce and there are no explicit negative labels. The learning algorithm must
thus select which unlabeled examples to use as negative training points,
possibly ending up with an unbalanced learning problem. We address these issues
by proposing an algorithm that combines active learning (for selecting negative
examples) with imbalance-aware learning (for mitigating the label imbalance).
In our experiments we observe that these two techniques operate
synergistically, outperforming state-of-the-art methods on standard protein
function prediction benchmarks.
