[BibTeX] [RIS]
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Type of publication: Inproceedings
Citation: yk:Relaxed-Pruning
Booktitle: Discovery Science
Year: 2019
Month: October
Pages: 367--382
Publisher: Springer International Publishing
Note: Best Student Paper Award
ISBN: 978-3-030-33778-0
URL: https://arxiv.org/abs/1908.06874
DOI: 10.1007/978-3-030-33778-0_28
Abstract: Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.
Keywords: Label Dependencies, multilabel classification, Rule Learning
Authors Klein, Yannik
Rapp, Michael
Loza Mencía, Eneldo
Editors Kralj Novak, Petra
Šmuc, Tomislav
D{\v z}eroski, Sašo