Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Information Theory and Reliable Communication
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
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The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
Efficient Pairwise Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A review on the combination of binary classifiers in multiclass problems
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On the Decoding Process in Ternary Error-Correcting Output Codes
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Polychotomous classification with pairwise classifiers: a new voting principle
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Decoding of ternary error correcting output codes
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Decoding rules for error correcting output code ensembles
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Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
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Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this article, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies.