Rapp, Michael ; Loza Mencía, Eneldo ; Fürnkranz, Johannes
Hrsg.: Kralj Novak, Petra ; Šmuc, Tomislav ; Džeroski, Sašo (2019)
On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics.
DS'19 - 22nd International Conference on Discovery Science. Split, Croatia (October 28.–30., 2019)
doi: 10.1007/978-3-030-33778-0_9
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Kurzbeschreibung (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.

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