Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification - Chmielnicki, Wiesław; Stąpor, Katarzyna (Zielona Góra: Uniwersytet Zielonogórski)
The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes.
Keywords: pairwise coupling; multi-class classification; problem decomposition; support vector machines