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.