A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data

Kamal Premaratne, Manohar Murthi, Jinsong Zhang, Matthias Scheutz, Peter H. Bauer

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

40 Citations (Scopus)

Abstract

Fusion of hard data with soft data is an issue that has attracted recent attention. An effective fusion strategy requires an analytical framework that can capture the uncertainty inherent in hard and soft data. For instance, computational linguistic parsing of text-based data generates logical propositions that inherently possess significant semantic ambiguity. An effective fusion framework must exploit the respective advantages of hard and soft data while mitigating their particular weaknesses. In this paper, we describe a Dempster-Shafer theoretic approach to hard and soft data fusion that relies upon the novel conditional approach to updating. The conditional approach engenders a more flexible method that allows for tuning and adapting update strategies. When computational complexity concerns are taken into account, it also provides guidance on how evidence could be ordered for updating. This has important implications in working with models that convert propositional logic statements from text into Dempster-Shafer theoretic form.

Original languageEnglish
Title of host publication2009 12th International Conference on Information Fusion, FUSION 2009
Pages2122-2129
Number of pages8
StatePublished - Nov 18 2009
Event2009 12th International Conference on Information Fusion, FUSION 2009 - Seattle, WA, United States
Duration: Jul 6 2009Jul 9 2009

Other

Other2009 12th International Conference on Information Fusion, FUSION 2009
CountryUnited States
CitySeattle, WA
Period7/6/097/9/09

Fingerprint

Computational linguistics
Data fusion
Computational complexity
Tuning
Semantics
Uncertainty

Keywords

  • Conditional approach
  • Dempster-Shafer theory
  • Evidence fusion
  • Evidence updating
  • Soft information

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software

Cite this

Premaratne, K., Murthi, M., Zhang, J., Scheutz, M., & Bauer, P. H. (2009). A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data. In 2009 12th International Conference on Information Fusion, FUSION 2009 (pp. 2122-2129). [5203683]

A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data. / Premaratne, Kamal; Murthi, Manohar; Zhang, Jinsong; Scheutz, Matthias; Bauer, Peter H.

2009 12th International Conference on Information Fusion, FUSION 2009. 2009. p. 2122-2129 5203683.

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

Premaratne, K, Murthi, M, Zhang, J, Scheutz, M & Bauer, PH 2009, A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data. in 2009 12th International Conference on Information Fusion, FUSION 2009., 5203683, pp. 2122-2129, 2009 12th International Conference on Information Fusion, FUSION 2009, Seattle, WA, United States, 7/6/09.
Premaratne K, Murthi M, Zhang J, Scheutz M, Bauer PH. A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data. In 2009 12th International Conference on Information Fusion, FUSION 2009. 2009. p. 2122-2129. 5203683
Premaratne, Kamal ; Murthi, Manohar ; Zhang, Jinsong ; Scheutz, Matthias ; Bauer, Peter H. / A Dempster-Shafer theoretic conditional approach to evidence updating for fusion of hard and soft data. 2009 12th International Conference on Information Fusion, FUSION 2009. 2009. pp. 2122-2129
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