Applying Weights in the Functioning of the Dynamic Classifier Selection Method

Authors:
Diogo Fagundes;Anne Canuto
Affiliations:
Federal University of Rio Grande do Norte(UFRN), Brazil;Federal University of Rio Grande do Norte(UFRN), Brazil
Venue:
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Year:
2006

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Abstract

There are two main approaches to combine the output of classifiers within a multi-classifier system, which are: combination-based and selection-based methods. In selectionbased methods, only one classifier is needed to correctly classify the input pattern. The choice of a classifier is typically based on the certainty of the current decision. On the other hand, the use of weights can be very useful for the final decision of a multi-classifier system since it can provide a confidence degree for each classifier. This paper presents an investigation of using two confidence measures (weights) in the functioning of the dynamic classifier method, which is a selection-based method. The main aim of this paper is to analyze the benefits of using weights in a selection-based method and which one is more suitable to be used.