Hierarchical proportional redistribution for bba approximation

Jean Dezert, Deqiang Han, Zhunga Liu, Jean Marc Tacnet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

Dempster's rule of combination is commonly used in the field of information fusion when dealing with belief functions. However, it generally requires a high computational cost. To reduce it, a basic belief assignment (bba) approximation is needed. In this paper we present a new bba approximation approach called hierarchical proportional redistribution (HPR) allowing to approximate a bba at any given level of non-specificity. Two examples are given to show how our new HPR works.

Original languageEnglish
Title of host publicationBelief Functions
Subtitle of host publicationTheory and Applications - Proceedings of the 2nd International Conference on Belief Functions
Pages275-283
Number of pages9
DOIs
StatePublished - 2012
Event2nd International Conferenceon Belief Functions - Compiegne, France
Duration: 9 May 201211 May 2012

Publication series

NameAdvances in Intelligent and Soft Computing
Volume164 AISC
ISSN (Print)1867-5662

Conference

Conference2nd International Conferenceon Belief Functions
Country/TerritoryFrance
CityCompiegne
Period9/05/1211/05/12

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