Abstract
In the last decade the classifier ensembles have enjoyed a growing attention and popularity due to their properties and successful applications.
A number of combination techniques, including majority vote, average vote, behaviorknowledge space, etc. are used to amplify correct decisions of the ensemble members. But the key of the success of classifier fusion is diversity of the combined classifiers.
In this paper we compare the most commonly used combination rules and discuss their relationship with diversity of individual classifiers.
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Gatnar, E. (2008). Fusion of Multiple Statistical Classifiers. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_3
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DOI: https://doi.org/10.1007/978-3-540-78246-9_3
Publisher Name: Springer, Berlin, Heidelberg
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