  Belief function theory provides a flexible way to combine information
provided by different sources. This combination is usually followed by a
decision making which can be handled by a range of decision rules. Some rules
help to choose the most likely hypothesis. Others allow that a decision is made
on a set of hypotheses. In [6], we proposed a decision rule based on a distance
measure. First, in this paper, we aim to demonstrate that our proposed decision
rule is a particular case of the rule proposed in [4]. Second, we give
experiments showing that our rule is able to decide on a set of hypotheses.
Some experiments are handled on a set of mass functions generated randomly,
others on real databases.
