Pairwise fusion matrix for combining classifiers

Authors:
Albert H. R. Ko;Robert Sabourin;Alceu de Souza Britto, Jr.;Luiz Oliveira
Affiliations:
LIVIA, ícole de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Que., Canada H3C 1K3;LIVIA, ícole de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Que., Canada H3C 1K3;PPGIA, Pontifical Catholic University of Parana, Rua Imaculada Conceicao, 1155, PR 80215-901, Curitiba, Brazil;PPGIA, Pontifical Catholic University of Parana, Rua Imaculada Conceicao, 1155, PR 80215-901, Curitiba, Brazil
Venue:
Pattern Recognition
Year:
2007

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Abstract

Various fusion functions for classifier combination have been designed to optimize the results of ensembles of classifiers (EoC). We propose a pairwise fusion matrix (PFM) transformation, which produces reliable probabilities for the use of classifier combination and can be amalgamated with most existent fusion functions for combining classifiers. The PFM requires only crisp class label outputs from classifiers, and is suitable for high-class problems or problems with few training samples. Experimental results suggest that the performance of a PFM can be a notch above that of the simple majority voting rule (MAJ), and a PFM can work on problems where a behavior-knowledge space (BKS) might not be applicable.