Improved Pairwise Coupling Classification With Correcting Classifiers

The benefits obtained from the decomposition of a classification task involving several classes, into a set of smaller classification problems involving two classes only, usually called dichotomies, have been exposed in various occasions. Among the multiple ways of applying the referred decomposition, Pairwise Coupling is one of the best known. Its principle is to separate, in each binary subproblem, a pair of classes, ignoring the remaining ones, which causes the decomposition scheme to contain as much subproblems as the number of possible pairs of classes in the original task. Pairwise Coupling decomposition has so far been used in different applications. In this paper, various ways of recombining the outputs of all the classifiers solving the existing subproblems are explored, and an important handicap of its intrinsic nature is exposed, which consists in the use, for the classification, of impertinent information. A solution for this problem is suggested and it is shown how it can significantly improve the classification accuracy. In addition, a powerful decomposition scheme derived from the proposed correcting procedure is presented.

Proceedings of the 10th European Conference on Machine Learning, 1998

 Record created 2006-03-10, last modified 2018-03-17

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