[BibTeX] [RIS]
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
Type of publication: Inproceedings
Citation: mr:ML-Consistency-Coverage
Booktitle: Discovery Science
Year: 2019
Month: October
Pages: 96--111
Publisher: Springer International Publishing
Address: Cham
ISBN: 978-3-030-33778-0
URL: https://arxiv.org/abs/1908.03032
DOI: 10.1007/978-3-030-33778-0_9
Abstract: 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.
Keywords: heuristics, multilabel classification, Rule Learning
Authors Rapp, Michael
Loza Mencía, Eneldo
Fürnkranz, Johannes
Editors Kralj Novak, Petra
Šmuc, Tomislav
D{\v z}eroski, Sašo