Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Intelligent Data Analysis
Polychotomous classification with pairwise classifiers: a new voting principle
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
How to do multi-way classification with two-way classifiers
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Multilabel classification via calibrated label ranking
Machine Learning
Improving the Run-Time Performance of Multi-class Support Vector Machines
Proceedings of the 30th DAGM symposium on Pattern Recognition
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
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
Improving Pit---Pattern Classification of Endoscopy Images by a Combination of Experts
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Multiclass mineral recognition using similarity features and ensembles of pair-wise classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Dual layer voting method for efficient multi-label classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
Two stage architecture for multi-label learning
Pattern Recognition
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
Efficient multilabel classification algorithms for large-scale problems in the legal domain
Semantic Processing of Legal Texts
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
Efficient pairwise classification using local cross off strategy
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A subspace approach to error correcting output codes
Pattern Recognition Letters
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Pairwise classification is a class binarization procedure that converts a multi-class problem into a series of two-class problems, one problem for each pair of classes. While it can be shown that for training, this procedure is more efficient than the more commonly used one-against-all approach, it still has to evaluate a quadratic number of classifiers when computing the predicted class for a given example. In this paper, we propose a method that allows a faster computation of the predicted class when weighted or unweighted voting are used for combining the predictions of the individual classifiers. While its worst-case complexity is still quadratic in the number of classes, we show that even in the case of completely random base classifiers, our method still outperforms the conventional pairwise classifier. For the more practical case of well-trained base classifiers, its asymptotic computational complexity seems to be almost linear.