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.
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Yamaguchi, N. (2006). Combining Pairwise Coupling Classifiers Using Individual Logistic Regressions. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_2
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DOI: https://doi.org/10.1007/11893257_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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