Classification by Pairwise Coupling
Abstract
We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then cou(cid:173) pling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the na(cid:173) ture of the class probability estimates that arise, and examine the performance of the procedure in simulated datasets. The classifiers used include linear discriminants and nearest neighbors: applica(cid:173) tion to support vector machines is also briefly described.
Cite
Text
Hastie and Tibshirani. "Classification by Pairwise Coupling." Neural Information Processing Systems, 1997.Markdown
[Hastie and Tibshirani. "Classification by Pairwise Coupling." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/hastie1997neurips-classification/)BibTeX
@inproceedings{hastie1997neurips-classification,
title = {{Classification by Pairwise Coupling}},
author = {Hastie, Trevor and Tibshirani, Robert},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {507-513},
url = {https://mlanthology.org/neurips/1997/hastie1997neurips-classification/}
}