Authors: Eyke Hüllermeier1 and
Fachbereich Mathematik und Informatik
Philipps-Universität Marburg, Germany
Technische Universität Darmstadt, Germany
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
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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