Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
This work addresses a technical bottleneck in multi-label classification for researchers and practitioners, but it appears incremental as it focuses on analyzing existing metrics rather than proposing a new method.
The paper tackles the challenge of inducing multi-label rules by examining whether common multi-label evaluation metrics satisfy anti-monotonicity or decomposability properties to prune the exponential search space for label combinations, but no concrete results or numbers are provided.
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.