Alternative Decomposition Techniques for Label Ranking

Abstract

This work focuses on label ranking, a particular task of preference learning, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels. This paper discusses and proposes alternative reduction techniques that decompose the original problem into binary classification related to pairs of labels and that can take into account label correlation during the learning process. © Springer International Publishing Switzerland 2014.