Titre :

Interpreting evidential distances by connecting them to partial orders: Application to belief function approximation

Auteur(s) :

Klein, John [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Destercke, Sébastien [Auteur]
Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques" [Labex MS2T]
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Colot, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

International Journal of Approximate Reasoning

Pagination :

15-33

Éditeur :

Elsevier

Date de publication :

2016-04

ISSN :

0888-613X

Mot(s)-clé(s) en anglais :

Belief functions
distance
metric
partial order
semantics
convex optimization

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

The many distances defined in evidence theory provide instrumental tools to analyze and compare mass functions: they have been proposed to measure conflict, dependence or similarity in different fields (information fusion ...
Lire la suite >The many distances defined in evidence theory provide instrumental tools to analyze and compare mass functions: they have been proposed to measure conflict, dependence or similarity in different fields (information fusion , risk analysis, machine learning). Many of their mathematical properties have been studied in the past years, yet a remaining question is to know what distance to choose in a particular problem. As a step towards answering this question, we propose to interpret distances by looking at their consistency with partial orders possessing a clear semantic. We focus on the case of in-formational partial order and on the problem of approximating initial belief functions by simpler ones. Doing so, we study which distances can be used to measure the difference of informational content between two mass functions, and which distances cannot.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Sorbonne Universités à Paris pour l'Enseignement et la Recherche

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL