Probability Estimates for Multi-Class Classification by Pairwise Coupling
Abstract
Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998).
Cite
Text
Wu et al. "Probability Estimates for Multi-Class Classification by Pairwise Coupling." Journal of Machine Learning Research, 2004.Markdown
[Wu et al. "Probability Estimates for Multi-Class Classification by Pairwise Coupling." Journal of Machine Learning Research, 2004.](https://mlanthology.org/jmlr/2004/wu2004jmlr-probability/)BibTeX
@article{wu2004jmlr-probability,
title = {{Probability Estimates for Multi-Class Classification by Pairwise Coupling}},
author = {Wu, Ting-Fan and Lin, Chih-Jen and Weng, Ruby C.},
journal = {Journal of Machine Learning Research},
year = {2004},
pages = {975-1005},
volume = {5},
url = {https://mlanthology.org/jmlr/2004/wu2004jmlr-probability/}
}