TY  - CONF
ID  - yk:Relaxed-Pruning
T1  - Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
A1  - Klein, Yannik
A1  - Rapp, Michael
A1  - Loza Mencía, Eneldo
ED  - Kralj Novak, Petra
ED  - Šmuc, Tomislav
ED  - D{\v z}eroski, Sašo
TI  - Discovery Science
Y1  - 2019
SP  - 367
EP  - 382
PB  - Springer International Publishing
SN  - 978-3-030-33778-0
N1  - Best Student Paper Award
UR  - https://arxiv.org/abs/1908.06874
M2  - doi: 10.1007/978-3-030-33778-0_28
KW  - Label Dependencies
KW  - multilabel classification
KW  - Rule Learning
N2  - 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.
ER  -