Combining pairwise coupling classifiers using individual logistic regressions

Authors:
Nobuhiko Yamaguchi
Affiliations:
Faculty of Science and Engineering, Saga University, Saga-shi, Japan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

Pairwise coupling is a popular multi-class classification approach that prepares binary classifiers separating each pair of classes, and then combines the binary classifiers together. This paper proposes a pairwise coupling combination strategy using individual logistic regressions (ILR-PWC). We show analytically and experimentally that the ILR-PWC approach is more accurate than the individual logistic regressions.