Pairwise classification and support vector machines
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Reducing multiclass to binary: a unifying approach for margin classifiers
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Multi-Classification by Using Tri-Class SVM
Neural Processing Letters
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ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Intelligent Data Analysis
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Label ranking by learning pairwise preferences
Artificial Intelligence
Multilabel classification via calibrated label ranking
Machine Learning
On Pairwise Naive Bayes Classifiers
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient Pairwise Classification
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Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
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Binary Decomposition Methods for Multipartite Ranking
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Journal of Computer and System Sciences
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Preference Learning
On exploiting hierarchical label structure with pairwise classifiers
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Efficient prediction algorithms for binary decomposition techniques
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IEEE Transactions on Neural Networks
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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.