Applying pairwise fusion matrix on fusion functions for classifier combination

Authors:
Albert Hung-Ren Ko;Robert Sabourin;Alceu De Souza Britto, Jr.
Affiliations:
LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil;LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil;LIVIA, ETS, University of Quebec, Montreal, Quebec, Canada and PPGIA, Pontifical Catholic University of Parana, Curitiba, Brazil
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

We propose a new classifier combination scheme for the ensemble of classifiers. The Pairwise Fusion Matrix (PFM) constructs confusion matrices based on classifier pairs and thus offers the estimated probability of each class based on each classifier pair. These probability outputs can then be combined and the final outputs of the ensemble of classifiers is reached using various fusion functions. The advantage of this approach is the flexibility of the choice of the fusion functions, and the experiments suggest that the PFM combined with the majority voting outperforms the simple majority voting scheme on most of problems.