A Distance-Based Decision in the Credal Level
This work is incremental, refining decision-making methods in belief function theory for applications in information fusion.
The paper demonstrates that a previously proposed distance-based decision rule for belief function theory is a special case of an existing rule, and shows through experiments that it can effectively decide on sets of hypotheses using both random and real data.
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.