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Recently, several authors have advocated the use of rule learning algorithmsto model multi-label data, as rules are interpretable and can be comprehended,analyzed, or qualitatively evaluated by domain experts. Many rule learningalgorithms employ a heuristic-guided search for rules that model regularitiescontained in the training data and it is commonly accepted that the choice ofthe heuristic has a significant impact on the predictive performance of thelearner. Whereas the properties of rule learning heuristics have been studiedin the realm of single-label classification, there is no such work taking intoaccount the particularities of multi-label classification. This is surprising,as the quality of multi-label predictions is usually assessed in terms of avariety of different, potentially competing, performance measures that cannotall be optimized by a single learner at the same time. In this work, we showempirically that it is crucial to trade off the consistency and coverage ofrules differently, depending on which multi-label measure should be optimizedby a model. Based on these findings, we emphasize the need for configurablelearners that can flexibly use different heuristics. As our experiments reveal,the choice of the heuristic is not straight-forward, because a search for rulesthat optimize a measure locally does usually not result in a model thatmaximizes that measure globally.

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics