Abstract
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.
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Notes
- 1.
scene (6, 1.06), emotions (6, 1.87), flags (7, 3.39), yeast (14, 4.24), birds (19, 1.01), genbase (27, 1.25), medical (45, 1.24), cal500 (174, 26.15), with respective number of labels and cardinality, from http://mulan.sf.net. Source code and results are available at https://github.com/keelm/SeCo-MLC.
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We acknowledge support by the German Research Foundation (DFG) (grant number FU 580/11).
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Rapp, M., Loza Mencía, E., Fürnkranz, J. (2018). Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_3
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