Function approximation in the framework of evidence theory: a connectionist approach

We propose a novel approach to functional regression based on the transferable belief model, a variant of the Dempster-Shafer theory of evidence. This method uses reference vectors for computing a belief structure that quantifies the uncertainty attached to the prediction of the target data, given the input data. The procedure may be implemented in a neural network with specific architecture and adaptive weights. It allows to compute an imprecise assessment of the target data in the form of lower and upper expectations. The width of this interval reflects the partial indeterminacy of the prediction resulting from the relative scarcity of training data.