Multisource taxonomy-based classification using the transferable belief model

This paper addresses the problem of multi-source object classification in a context where objects of interest are part of a known taxonomy and the classification sources report at varying levels of specificity. This problem must consider several technical challenges: a) support fusion of heterogeneous classification inputs, b) provide a computationally scalable approach that accommodates taxonomy's with thousands of leaf nodes, and c) provide outputs that support tactical decision aides and are suitable inputs for subsequent fusion processes. This paper presents an approach that employs the Transferable Belief Model, Pignistic Transforms, and Bayesian Fusion to address these challenges.