Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
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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When dealing with multi-class classification tasks, a popular and applicable way is to decompose the original problem into a set of binary subproblems. The most well-known decomposition strategy is one-against-one and the corresponding widely-used method to recombine the outputs of all binary classifiers is pairwise coupling (PWC). However PWC has an intrinsic shortcoming; many meaningless partial classification results contribute to the global prediction result. In this paper, this problem is tackled by the use of correcting classifiers. A novel algorithm is proposed which works in two steps: First the original pairwise probabilities are converted into a new set of pairwise probabilities, then pairwise coupling is employed to construct the global posterior probabilities. This algorithm is applied to face recognition on the ORL face database, experimental results show that it is effective and efficient.