The common operation mechanism of multiple classifier systems is the combination of classifier outputs. Some researchers have 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 provides 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
A theoretical framework for dynamic classifier selection
GIACINTO, GIORGIO;ROLI, FABIO
2000-01-01
Abstract
The common operation mechanism of multiple classifier systems is the combination of classifier outputs. Some researchers have 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 provides 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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