Rule-Based Multi-label Classification: Challenges and Opportunities

Abstract

In the context of multi-label classification (MLC), rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this paper, we present a modular framework for rule-based MLC and discuss related challenges and opportunities for multi-label rule learning.