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. © 2011 Springer-Verlag.
CITATION STYLE
Hüllermeier, E., & Fürnkranz, J. (2011). Learning from label preferences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6926 LNAI, pp. 2–17). https://doi.org/10.1007/978-3-642-24477-3_2
Mendeley helps you to discover research relevant for your work.