Continuous belief functions and α-stable distributions

The theory of belief functions has been formalized in continuous domain for pattern recognition. Some applications use assumption of Gaussian models. However, this assumption is reductive. Indeed, some data are not symmetric and present property of heavy tails. It is possible to solve these problems by using a class of distributions called α-stable distributions. Consequently, we present in this paper a way to calculate pignistic probabilities with plausibility functions where the knowledge of the sources of information is represented by symmetric α-stable distributions. To validate our approach, we compare our results in special case of Gaussian distributions with existing methods. To illustrate our work, we generate arbitrary distributions which represents speed of planes and take decisions. A comparison with a Bayesian approach is made to show the interest of the theory of belief functions.