Case-Based Label Ranking

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propose a sophisticated k-NN framework as an alternative to previous binary decomposition techniques. It exhibits the appealing property of transparency and is based on an aggregation model which allows to incorporate a broad class of pairwise loss functions on label ranking. In addition to these conceptual advantages, we also present empirical results underscoring the merits of our approach in comparison to state-of-the-art learning methods.