Case-Based Label Ranking [chapter]

Klaus Brinker, Eyke Hüllermeier
2006 Lecture Notes in Computer Science  
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
more » ... lso present empirical results underscoring the merits of our approach in comparison to state-of-the-art learning methods.
doi:10.1007/11871842_53 fatcat:zcms462hgrezva6wsuao672bgu