Classification by Pairwise Coupling

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Trevor Hastie, Robert Tibshirani


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.