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
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. Moreira and Mayoraz suggested to tackle this problem by using correcting classifiers [4]. Though much better performance was obtained, their algorithm is simple and has some disadvantages. In this paper, we propose a novel algorithm 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. Employing support vector machines as binary classifiers, we perform investigation on several benchmark datasets. Experimental results show that our algorithm is effective and efficient.