TY - CONF ID - mr:ML-Antimonotonicity T1 - Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules A1 - Rapp, Michael A1 - Loza Mencía, Eneldo A1 - Fürnkranz, Johannes ED - Phung, Dinh Q. ED - Tseng, Vincent S. ED - Webb, Geoffrey I. ED - Ho, Bao ED - Ganji, Mohadeseh ED - Rashidi, Lida TI - PAKDD 2018: Advances in Knowledge Discovery and Data Mining Y1 - 2018 SP - 29 EP - 42 PB - Springer International Publishing AD - Cham SN - 978-3-319-93034-3 UR - https://arxiv.org/abs/1812.06833 M2 - doi: 10.1007/978-3-319-93034-3_3 N2 - Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads. ER -