Models of belief functions — Impacts for patterns recognitions

In a lot of operational situations, we have to deal with uncertain and inaccurate information. The theory of belief functions is a mathematical framework useful to handle this kind of imperfection. However, in most of the cases, uncertain data are modeled with a distribution of probability. We present in this paper different principles to induce belief functions from probabilities. Hence, we decide to use these functions in a pattern recognition problem. We discuss about the results we obtain according the way we generate the belief function. To illustrate our work, it will be applied to seabed characterization.