A Theoretical Framework for Dynamic Classifier Selection

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A Theoretical Framework for Dynamic Classifier Selection
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.
Giorgio Giacinto, Fabio Roli
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICPR
Authors Giorgio Giacinto, Fabio Roli
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