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Published Articles >> Table of Contents >> Abstract
15th International Conference on Pattern Recognition (ICPR'00) - Volume 2
p. 2008
A Theoretical Framework for Dynamic Classifier Selection
Giorgio Giacinto, University of Cagliari
Fabio Roli, University of Cagliari
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.906007
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At present, the common operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers pointed out the potentialities of “dynamic classifier selection” as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theoretical framework for dynamic classifier selection. To this end, dynamic classifier selection is placed in the general framework of statistical decision theory and it is showed that, under some assumptions, the optimal Bayes classifier can be obtained by the selection of non-optimal classifiers.
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 Additional Information
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Citation:
Giorgio Giacinto, Fabio Roli,
"A Theoretical Framework for Dynamic Classifier Selection,"
icpr,
p. 2008,
15th International Conference on Pattern Recognition (ICPR'00) - Volume 2,
2000
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