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.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Klaus Brinker, Eyke Hüllermeier
• Keywords: ECML 2006, Label Ranking, Machine Learning, Pairwise Loss Functions, Sophisticated K-NN Framework