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 |
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