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  -