Abstract
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 one to incorporate a variety of pairwise loss functions on label rankings. In addition to these conceptual advantages, we empirically show that our case-based approach is competitive to state-of-the-art model-based learners with respect to accuracy while being computationally much more efficient. Moreover, our approach suggests a natural way to associate confidence scores with predictions, a property not being shared by previous methods.
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Brinker, K., Hüllermeier, E. (2006). Case-Based Label Ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science(), vol 4212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871842_53
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DOI: https://doi.org/10.1007/11871842_53
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