  In recent years, multi-label classification has attracted a significant body
of research, motivated by real-life applications, such as text classification
and medical diagnoses. Although sparsely studied in this context, Learning
Classifier Systems are naturally well-suited to multi-label classification
problems, whose search space typically involves multiple highly specific
niches. This is the motivation behind our current work that introduces a
generalized multi-label rule format -- allowing for flexible label-dependency
modeling, with no need for explicit knowledge of which correlations to search
for -- and uses it as a guide for further adapting the general Michigan-style
supervised Learning Classifier System framework. The integration of the
aforementioned rule format and framework adaptations results in a novel
algorithm for multi-label classification whose behavior is studied through a
set of properly defined artificial problems. The proposed algorithm is also
thoroughly evaluated on a set of multi-label datasets and found competitive to
other state-of-the-art multi-label classification methods.
