Possibilistic Information Fusion Using Maximal Coherent Subsets

Didier Dubois, Sebastien Destercke, Eric Chojnacki

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

When multiple sources provide information about the same unknown quantity, their fusion into a synthetic interpretable message is often a tedious problem, especially when sources are conicting. In this paper, we propose to use possibility theory and the notion of maximal coherent subsets, often used in logic-based representations, to build a fuzzy belief structure that will be instrumental both for extracting useful information about various features of the information conveyed by the sources and for compressing this information into a unique possibility distribution. Extensions and properties of the basic fusion rule are also studied.
Original languageEnglish
Pages (from-to)79-92
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume17
Issue number1
DOIs
Publication statusPublished - Feb 2009

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Possibilistic Information Fusion Using Maximal Coherent Subsets'. Together they form a unique fingerprint.

Cite this