In this work, we present the general framework to apply active learning to multi-label data, discussing the key issues that need to be considered in pool-based multi-label active learning and how existing solutions in the literature deal with each of these issues. We further propose a novel aggregation method for evaluating which instances are to be annotated. Extensive experiments on thirteen multi-label data sets with different characteristics and under two different applications settings (transductive, inductive) convey a consistent advantage of our proposed approach against the rest of the approaches and, most importantly, against passive supervised learning and reveal interesting aspects related mainly to the properties of the data sets, and secondarily to the application settings.
