Applying Weights in the Functioning of the Dynamic Classifier Selection Method

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.