Learning from Label Preferences
(invited lecture for DS 2011)

Authors: Eyke Hüllermeier1 and Johannes Fürnkranz2

Affiliation1: Fachbereich Mathematik und Informatik
Philipps-Universität Marburg, Germany
2Fachbereich Informatik
Technische Universität Darmstadt, Germany

Abstract. In this paper, we review the framework of learning (from) label preferences, a particular instance of preference learning. Following an introduction to the learning setting, we particularly focus on our own work, which addresses this problem via the learning by pairwise comparison paradigm. From a machine learning point of view, learning by pairwise comparison is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We also discuss how a number of common machine learning tasks, such as multi-label classification, hierarchical classification or ordinal classification, may be addressed within the framework of learning from label preferences. We also briefly address theoretical questions as well as algorithmic and complexity issues.

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