Consensus-based credibility estimation of soft evidence for robust data fusion

Thanuka L. Wickramarathne, Kamal Premaratne, Manohar N. Murthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Due to its subjective naturewhich can otherwise compromise the integrity of the fusion process, it is critical that soft evidence (generated by human sources) be validated prior to its incorporation into the fusion engine. The strategy of discounting evidence based on source reliability may not be applicable when dealing with soft sources because their reliability (e.g., an eye witnesses account) is often unknown beforehand. In this paper, we propose a methodology based on the notion of consensus to estimate the credibility of (soft) evidence in the absence of a 'ground truth.' This estimated credibility can then be used for source reliability estimation, discounting or appropriately 'weighting' evidence for fusion. The consensus procedure is set up via Dempster-Shafer belief theoretic notions. Further, the proposed procedure allows one to constrain the consensus by an estimate of the ground truth if/when it is available. We illustrate several interesting and intuitively appealing properties of the consensus procedure via a numerical example.

Original languageEnglish (US)
Title of host publicationBelief Functions
Subtitle of host publicationTheory and Applications - Proceedings of the 2nd International Conference on Belief Functions
Pages301-309
Number of pages9
DOIs
StatePublished - May 18 2012
Event2nd International Conferenceon Belief Functions - Compiegne, France
Duration: May 9 2012May 11 2012

Publication series

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

Other

Other2nd International Conferenceon Belief Functions
CountryFrance
CityCompiegne
Period5/9/125/11/12

ASJC Scopus subject areas

  • Computer Science(all)

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    Wickramarathne, T. L., Premaratne, K., & Murthi, M. N. (2012). Consensus-based credibility estimation of soft evidence for robust data fusion. In Belief Functions: Theory and Applications - Proceedings of the 2nd International Conference on Belief Functions (pp. 301-309). (Advances in Intelligent and Soft Computing; Vol. 164 AISC). https://doi.org/10.1007/978-3-642-29461-7_35