A Distance-Based Decision in the Credal Level

Amira Essaid (1, 2) , Arnaud Martin (2) , Grégory Smits (3) , Boutheina Ben Yaghlane (1)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement

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.

Mots clés: belief function theory; imprecise decision; distance

Domaines: Intelligence artificielle [cs.AI]

Amira Essaid, Arnaud Martin, Grégory Smits, Boutheina Ben Yaghlane. A Distance-Based Decision in the Credal Level. International Conference on Artificial Intelligence and Symbolic Computation (AISC 2014), Dec 2014, Sevilla, Spain. pp.147 - 156, ⟨10.1007/978-3-319-13770-4_13⟩. ⟨hal-01110349⟩