Abstract
There are two main approaches to combine the output of classifiers within a multi-classifier system (MCS), which are: combination-based and selection-based methods. In selection-based 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. The use of weights can be very useful for the final decision of a selection-based MCS since it can provide a confidence degree for each classifier. This paper presents the use of two confidence measures applied in three selection-based methods. The main aim of this paper is to analyze the benefits of using weights in the main selection-based methods and which confidence measure is more suitable to be used.