Artificial Intelligence
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Decision making under uncertainty using imprecise probabilities
International Journal of Approximate Reasoning
Joint propagation of probability and possibility in risk analysis: Towards a formal framework
International Journal of Approximate Reasoning
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Correcting binary imprecise classifiers: local vs global approach
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Hi-index | 0.00 |
This paper proposes a simple framework to combine binary classifiers whose outputs are imprecise probabilities (or are transformed into some imprecise probabilities, e.g., by using confidence intervals). This combination comes down to solve linear programs describing constraints over events (here, subsets of classes). The number of constraints grows linearly with the number of classifiers, making the proposed framework tractable for problems involving a relatively large number of classes. After detailing the method, we provide some first experimental results illustrating the method interests.