TY - CONF ID - mr:ML-Consistency-Coverage T1 - On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics A1 - Rapp, Michael A1 - Loza Mencía, Eneldo A1 - Fürnkranz, Johannes ED - Kralj Novak, Petra ED - Šmuc, Tomislav ED - D{\v z}eroski, Sašo TI - Discovery Science Y1 - 2019 SP - 96 EP - 111 PB - Springer International Publishing AD - Cham SN - 978-3-030-33778-0 UR - https://arxiv.org/abs/1908.03032 M2 - doi: 10.1007/978-3-030-33778-0_9 KW - heuristics KW - multilabel classification KW - Rule Learning N2 - 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. ER -