In the theory of belief functions, many measures
of uncertainty have been introduced. However, it is not always
easy to understand what these measures really try to represent.
In this paper, we re-interpret some measures of uncertainty in
the theory of belief functions. We present some interests and
drawbacks of the existing measures. On these observations, we
introduce a measure of contradiction. Therefore, we present some
degrees of non-specificity and Bayesianity of a mass. We propose
a degree of specificity based on the distance between a mass and
its most specific associated mass. We also show how to use the
degree of specificity to measure the specificity of a fusion rule.
Illustrations on simple examples are given.
Category: Artificial Intelligence