Robust Multi-label Classification Via Preference Learning

In this paper, we explore how multi-label classification (MLC) tasks can be cast into order structure learning. Our motivation for doing so is to exploit the very rich structure of the orders to improve and robustify MLC learning. We describe formally how MLC can be transformed into an order structure learning and prediction task, and then proceed to study the problem of predicting Bayes-optimal order structures. We then perform some experiments in settings where the use of order structures can be very beneficial: robust MLC in the presence of noisy and imbalanced labels, and making MLC predictions with partial abstention.