Pairwise classification and support vector machines
Advances in kernel methods
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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
Artificial Intelligence Review
A Bayesian Approximation Method for Online Ranking
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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A multi-class classification problem can be solved efficiently via decomposition of the problem into multiple binary classification problems. As a way of such decomposition, we propose a novel pairwise coupling method based on the TrueSkill ranking system. Instead of aggregating all pairwise binary classification results for the final decision, the proposed method keeps track of the ranks of the classes during the successive binary classification procedure. Especially, selection of a binary classifier at a certain step is done in such a way that the multi-class classification decision using the binary classification results up to the step converges to the final one as quickly as possible. Thus, the number of binary classifications can be reduced, which in turn reduces the computational complexity of the whole classification system. Experimental results show that the complexity is reduced significantly for no or minor loss of classification performance.