A Distance-Based Decision in the Credal Level

TL;DR

This paper explores a distance-based decision rule within belief function theory, demonstrating its relation to existing rules and showcasing its ability to decide on sets of hypotheses through experiments on both synthetic and real data.

Contribution

It shows that the proposed decision rule is a special case of a known rule and demonstrates its effectiveness in decision-making on hypothesis sets.

Findings

The decision rule is a particular case of a known rule.

The rule can decide on sets of hypotheses.

Experimental results validate the rule's effectiveness.

Abstract

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.