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.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia
Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further
information. arXiv admin note: text overlap with arXiv:1812.0005