  Multi-label classification consists in classifying an instance into two or
more classes simultaneously. It is a very challenging task present in many
real-world applications, such as classification of biology, image, video,
audio, and text. Recently, the interest in interpretable classification models
has grown, partially as a consequence of regulations such as the General Data
Protection Regulation. In this context, we propose a multi-objective
evolutionary algorithm that generates multiple rule-based multi-label
classification models, allowing users to choose among models that offer
different compromises between predictive power and interpretability. An
important contribution of this work is that different from most algorithms,
which usually generate models based on lists (ordered collections) of rules,
our algorithm generates models based on sets (unordered collections) of rules,
increasing interpretability. Also, by employing a conflict avoidance algorithm
during the rule-creation, every rule within a given model is guaranteed to be
consistent with every other rule in the same model. Thus, no conflict
resolution strategy is required, evolving simpler models. We conducted
experiments on synthetic and real-world datasets and compared our results with
state-of-the-art algorithms in terms of predictive performance (F-Score) and
interpretability (model size), and demonstrate that our best models had
comparable F-Score and smaller model sizes.
