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Linguistic attribute hierarchies for downwards propagation of information

We investigate the propagation of label information for multi-attribute decision making problems downwards a linguistic attribute hierarchy, which represents the complex and often imprecise functional relationships between low level attributes or measurements and high-level decision or classification variables. The downward propagation algorithm identifies the branches in linguistic decision trees for which the probability of a high-level goal exceeds a given threshold. The sensitivity of the method to this threshold is then reduced by integrating with respect to a probability distribution on high threshold values.