Dynamic estimation of evidence discounting rates based on information credibility

M. C. Florea; A.-L. Jousselme; É. Bossé

RAIRO - Operations Research (2010)

  • Volume: 44, Issue: 4, page 285-306
  • ISSN: 0399-0559

Abstract

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Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(In) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(Sk), used then as a discounting factor for all further belief functions provided by Sk. The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object.

How to cite

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Florea, M. C., Jousselme, A.-L., and Bossé, É.. "Dynamic estimation of evidence discounting rates based on information credibility." RAIRO - Operations Research 44.4 (2010): 285-306. <http://eudml.org/doc/250710>.

@article{Florea2010,
abstract = { Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(In) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(Sk), used then as a discounting factor for all further belief functions provided by Sk. The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object. },
author = {Florea, M. C., Jousselme, A.-L., Bossé, É.},
journal = {RAIRO - Operations Research},
keywords = {Dempster-Shafer theory; evidence theory; discounting; credibility; fusion architecture},
language = {eng},
month = {12},
number = {4},
pages = {285-306},
publisher = {EDP Sciences},
title = {Dynamic estimation of evidence discounting rates based on information credibility},
url = {http://eudml.org/doc/250710},
volume = {44},
year = {2010},
}

TY - JOUR
AU - Florea, M. C.
AU - Jousselme, A.-L.
AU - Bossé, É.
TI - Dynamic estimation of evidence discounting rates based on information credibility
JO - RAIRO - Operations Research
DA - 2010/12//
PB - EDP Sciences
VL - 44
IS - 4
SP - 285
EP - 306
AB - Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(In) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(Sk), used then as a discounting factor for all further belief functions provided by Sk. The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object.
LA - eng
KW - Dempster-Shafer theory; evidence theory; discounting; credibility; fusion architecture
UR - http://eudml.org/doc/250710
ER -

References

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  1. J. Besombes, V. Nimier and L. Cholvy, Information Evaluation in Fusion using Information Correlation, in Proc. of the 12th International Conference on Information Fusion, Seattle, USA (2009).  
  2. L. Cholvy and V. Nimier, Information Evaluation: discussion about STANAG 2022 recommandations, in Military Data and Information Fusion, RTO-MP-IST-040, Prague, Czech Republic (2003).  
  3. F. Cuzzolin, A Geometric Approach to the Theory of Evidence. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews38 (2008) 522–534.  
  4. Y. Deng, W. Shi, Z. Zhu and Q. Liu, Combining belief functions based on distance of evidence. Decis. Support Syst.38 (2004) 489–493.  
  5. J. Diaz, M. Rifqi and B. Bouchon-Meunier, A similarity measure between basic belief assignments, in Proc. of the 9th International Conference Information Fusion, Firenze, Italy (2006).  
  6. M.C. Florea, E. Bossé and A.-L. Jousselme, Metrics, Distances and Dissimilarity Measures within Dempster-Shafer Theory to Characterize Sources' Reliability, in Proc. of Cognitive Systems with Interactive Sensors (COGIS '09) (2009).  
  7. M.C. Florea and E. Bossé, Crisis Management Using Dempster Shafer Theory: Using Dissimilarity Measures to Characterize Sources' Reliability, in Proc. of C3I in Crisis, Emergency and Consequence Management, RTO-MP-IST-086, Bucharest, Romania (2009).  
  8. M.C. Florea, A.-L. Jousselme, D. Grenier and E. Bossé, Robust combination rules for evidence theory. Inform. Fusion10 (2009) 183–197.  
  9. H. Guo, W. Shi and Y. Deng, Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics36 (2006) 970–981.  
  10. A.-L. Jousselme and P. Maupin, On some properties of distances in evidence theory, in Proc. of the Workshop on the Theory of Belief Functions, Brest, France (2010).  
  11. A.-L. Jousselme, D. Grenier and E. Bossé, A new distance between two bodies of evidence. Inform. Fusion2 (2001) 91–101.  
  12. W. Liu, Analyzing the degree of conflict among belief functions. Artif. Intell.170 (2006) 909–924.  
  13. R.C. Luo and M.G. Kay, Multisensor Integration and Fusion in Intelligent Systems. IEEE Transactions on Systems, Man, and Cybernetics19 (1989) 901–931.  
  14. A. Martin, A.-L. Jousselme and C. Osswald, Conflict measure for the discounting operation on belief functions, in Proc. of the 11th Annual Conference on Information Fusion, Cologne, Germany (2008).  
  15. D. Mercier, B. Quost and T. Denœux, Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inform. Fusion9 (2008) 246–258.  
  16. V. Nimier, Information Evaluation: a formalisation of operational recommandations, in Proc. of the 7th International Conference on Information Fusion, Stockholm, Sweden (2004).  
  17. B. Ristic and P. Smets, The TBM global distance measure for the association of uncertain combat ID declarations. Inform. Fusion7 (2006) 276–284.  
  18. B. Ristic and P. Smets, Global cost of assignment in the TBM framework for association of uncertain ID reports. Aerosp. Sci. Technol.11 (2007) 303–309 .  
  19. G. Rogova and E. Bossé, Information quality in information fusion, in Proc. of the 13th International Conference on Information Fusion, Edinburgh, Scotland (2010).  
  20. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press (1976).  
  21. P. Smets, Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. Int. J. Approxim. Reason.9 (1993) 1–35.  
  22. B. Tessem, Approximations for efficient computation in the theory of evidence. Artif. Intell.61 (1993) 315–329.  
  23. C. Wen, Y. Wang and X. Xu, Fuzzy Information Fusion Algorithm of Fault Diagnosis Based on Similarity Measure of Evidence, in Advances in Neural Networks, Ser. Lect. Notes Comp. Sci. 5264, Springer Berlin/Heidelberg (2008), pp. 506–515.  
  24. G. Xu, W. Tian, L. Qian and X. Zhang, A novel conflict reassignment method based on grey relational analysis (GRA). Pattern Recogn. Lett.28 (2007) 2080–2087.  
  25. R.R. Yager, On considerations of credibility of evidence. Int. J. Approxim. Reason.7 (1992) 45–72.  
  26. S. Young and J. Palmer, Pedigree and confidence: Issues in data credibility and reliability, in Proc. of the 10th International Conference on Information Fusion, Quebec city (Qc), Canada (2007).  

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