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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ko, A. H. R., Sabourin, R., & De Souza Britto, A. (2007). Applying pairwise fusion matrix on fusion functions for classifier combination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4472 LNCS, pp. 302–311). Springer Verlag. https://doi.org/10.1007/978-3-540-72523-7_31
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