Fusion of multi-level decision systems using the transferable belief model
David Mercier, G. Cron, Thierry Denœux, Marie-Hélène Masson · 2005
In this paper, we are interested in the fusion of classifiers providing decisions which are organized in a hierarchy, i.e., for each pattern to classify, each classifier has the possibility to choose a class, a set of classes, or a reject option. We present a method to combine these decisions based on the transferable belief model (TBM), an interpretation of the Dempster-Shafer theory of evidence. The TBM is shown to provide a powerful and flexible framework, well suited to this problem. Special emphasis is put on the construction of basic belief assignments, an important issue which has not yet been fully explored in the literature. We propose an approach extending a former proposal made by Xu, Krzyzak and Suen (1992) in a simpler context. A rational decision modelling allowing different levels of decision is also presented. Finally, the proposed combination is compared experimentally to several simpler alternatives.