Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Efficient pairwise classification using local cross off strategy
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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For a complex multiclass problem, it is common to construct the multiclass classifier by combining the outputs of several binary ones. The two basic methods for this purpose are known as one-per-class (OPC) and pairwise coupling (PWC) and their general form is error correcting output code (ECOC). In this paper, we review basic decomposition methods and introduce a new sequential fusion method based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with each basic method and ECOC method. The experimental results show that our proposed method can improve significantly the classification accuracy on the real dataset.