Reverse Multi-Label Learning
James Petterson and Tiberio Caetano
In: NIPS, 6-11 Dec 2010, Vancouver, Canada.
Multi-label classiﬁcation is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classiﬁcation and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classiﬁcation admit relaxations that can be efﬁciently optimised. We optimise these relaxations with standard algorithms and compare our results with several stateof-the-art methods, showing excellent performance.