Proceedings of the NIPS 2005 Workshop on Learning to Rank
In label-ranking, the goal is to learn a mapping from instances to rankings (total orders) over a fixed set of labels. Hitherto existing approaches to label-ranking implicitly operate on an underlying (utility) scale which is not calibrated, that is, which lacks a natural zero point. Since this severely restricts the expressive power of these approaches, we propose a suitable extension of the constraint classification framework to labelranking. Beyond the general case, we focus on a particular category of ranking problems which admit a representation as a special instance of calibrated label-ranking: In top label-ranking, an extension of multilabel classification an ordering is required for the subset of labels relevant for the given instance.