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ICIAP
1999
ACM

Methods for Dynamic Classifier Selection

10 years 1 months ago
Methods for Dynamic Classifier Selection
In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common “operation” mechanism of MCSs is the “combination” of classifiers outputs. Recently, some researchers pointed out the potentialities of “dynamic classifier selection” as a new operation mechanism. In a previous paper, the authors discussed the advantages of “selection-based” MCSs and proposed an algorithm for dynamic classifier selection [1]. In this paper, a theoretical framework for dynamic classifier selection is described and two methods for selecting classifiers are proposed. Reported results on the classification of different data sets show that dynamic classifier selection is an effective method for the development of MCSs.
Giorgio Giacinto, Fabio Roli
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where ICIAP
Authors Giorgio Giacinto, Fabio Roli
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