  Recently, several authors have advocated the use of rule learning algorithms
to model multi-label data, as rules are interpretable and can be comprehended,
analyzed, or qualitatively evaluated by domain experts. Many rule learning
algorithms employ a heuristic-guided search for rules that model regularities
contained in the training data and it is commonly accepted that the choice of
the heuristic has a significant impact on the predictive performance of the
learner. Whereas the properties of rule learning heuristics have been studied
in the realm of single-label classification, there is no such work taking into
account the particularities of multi-label classification. This is surprising,
as the quality of multi-label predictions is usually assessed in terms of a
variety of different, potentially competing, performance measures that cannot
all be optimized by a single learner at the same time. In this work, we show
empirically that it is crucial to trade off the consistency and coverage of
rules differently, depending on which multi-label measure should be optimized
by a model. Based on these findings, we emphasize the need for configurable
learners that can flexibly use different heuristics. As our experiments reveal,
the choice of the heuristic is not straight-forward, because a search for rules
that optimize a measure locally does usually not result in a model that
maximizes that measure globally.
