Interpreting evidential distances by connecting them to partial orders: Application to belief function approximation - Archive ouverte HAL Access content directly
Journal Articles International Journal of Approximate Reasoning Year : 2016

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

(1) , (2, 3) , (1)
1
2
3

Abstract

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.
Fichier principal
Vignette du fichier
submission_v0.pdf (1.05 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01263550 , version 1 (27-01-2016)

Identifiers

Cite

John Klein, Sébastien Destercke, Olivier Colot. Interpreting evidential distances by connecting them to partial orders: Application to belief function approximation. International Journal of Approximate Reasoning, 2016, 71, pp.15-33. ⟨10.1016/j.ijar.2016.01.001⟩. ⟨hal-01263550⟩
156 View
212 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More