Belief function combination: Comparative study within the classifier fusion framework

Abstract

Data fusion under the belief function framework has attracted the interest of many researchers over the past few years. Until now, many combination rules have been proposed in order to aggregate beliefs induced form dependent or independent information sources. Although the choice of the most appropriate rule among several alternatives is crucial, it still requires non-trivial effort. In this investigation, we suggest to evaluate and compare some combination rules when dealing with independent information sources in the context of the classifier fusion framework.