Combining Classifiers through Triplet-Based Belief Functions
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Fact reasoning with reliable D-S evidence theory
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Service selection in stochastic environments: a learning-automaton based solution
Applied Intelligence
A belief classification rule for imprecise data
Applied Intelligence
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In classifier combination, the relative values of beliefs assigned to different hypotheses are more important than accurate estimation of the combined belief function representing the joint observation space. Because of this, the independence requirement in Dempster's rule should be examined from classifier combination point of view. In this study, it is investigated whether there is a set of dependent classifiers which provides a better combined accuracy than independent classifiers when Dempster's rule of combination is used. The analysis carried out for three different representations of statistical evidence has shown that the combination of dependent classifiers using Dempster's rule may provide much better combined accuracies compared to independent classifiers.