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Research on Fusion of Dependent Evidence Based on Kendall Correlation Coefficient

Published: 20 October 2020 Publication History

Abstract

The Dempster-Shafer evidence theory (D-S evidence theory) has been applied in many fields for its superiority in dealing with uncertainty information and ignorance. However, independent evidence cannot be well dealt with by the classical D-S theory, which restricts its applications. This paper has proposed a method based on Kendall correlation coefficient for dealing with dependent evidence. Firstly, the Kendall correlation coefficient is used to calculate the degree of dependence between evidence; Secondly, the discount coefficient is the inverse of the degree of dependence; Finally, the total fusion result combines D-S theory combination rules and discount coefficients. Give an example to explain the proposed method in this article, and compare the recognition results with other methods, so as to illustrate the superiority of proposed method.

References

[1]
A P Dempster (1967). Upper and lower probabilities induced by a multivalued mapping[J]. Annals Mathematics Statistic, 38, 325--339.
[2]
G Shafer (1976). A mathematical theory of evidence.
[3]
L Si, Z Wang and G Jiang (2019). Fusion Recognition of Shearer Coal-rock Cutting State Based on Improved RBF Neural Network and D-S Evidence Theory[J]. IEEE Access, 7, 122106--122121.
[4]
G Lin, J Liang and Y Qian (2015). An information fusion approach by combining multigranulation rough sets and evidence theory[J]. Information Sciences, 314, 184--199.
[5]
X Y Su, S Mahadevan, P Xu and Y Deng (2015). Dependence assessment in human reliability analysis using evidence theory and AHP[J]. Risk Analysis, 35, 1296--1316.
[6]
Y Deng, R Sadiq, W Jiang and S Tesfamariam (2011). Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach[J]. Expert System Application, 38, 15438--15446.
[7]
M Beynon, B Curry and P Morgan (2000). The Dempster-Shafer theory of evidence: An alternative approach to multicriteria decision modelling[J]. Omega, 28, 37--50.
[8]
M Beynon, D Cosker and D Marshall (2001). An expert system for multi-criteria decision making using Dempster Shafer theory. Expert System Application[J]. 20, 357--367.
[9]
F Xiao (2019). EFMCDM: Evidential fuzzy multicriteria decision making based on belief entropy[J]. IEEE Transaction Fuzzy System, 1(1), 99.
[10]
H Guo, W Shi and Y Deng (2006). Evaluating sensor reliability in classification problems based on evidence theory[J]. IEEE Transaction System, 36, 970--981.
[11]
F Xiao (2020). A new divergence measure for belief functions in D-S evidence theory for multi-sensor data fusion[J]. Information Sciences, 514, 462--483.
[12]
T Deoeux (2008). Conjunctive and disjunctive combination of belief functions induced by non-distinct bodies of evidence. Artificial Intelligence, 172(2-3), 234--264.
[13]
C Fu (2014). Yang S. Conjunctive combination of belief functions from dependent source using positive and negative weight functions[J]. Expert Systems with Applications, 41(4), 1964--1972.
[14]
S Choenni and H E Blok (2006). Leertouwer E. Handling Uncertainty and Ignorance in Databases: A Rule to Combine Dependent Data[J]. Lecture Notes in Computer Science, 3882, 310--324.
[15]
P Smets and R Kennes (1992). The concept of distinct evidence[C]. In Proceeding of the 4th Conf on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU'92), Mallorca, Spain: Palma de Mayorca, 789--794.
[16]
T Denoeux (2008). Conjunctive and disjunctive combination of belief functions induced by non-distinct bodies of evidence. Artificial Intelligence, vol. 172(23), 234--264.
[17]
V Guralnik, D Mylaraswamy and H Voges (2006). On handling dependent evidence and multiple faults in knowledge fusion for engine health management[C]. In Proceedings of IEEE Aerospace Conference, Washington: IEEE Computer Society, 9--17.
[18]
X Su, P Xu and S Mahadevan (2014). On consideration of dependence and reliability of evidence in Dempster-Shafer theory[J]. Journal of Information & Computational Science, 11(14), 4901--4910.
[19]
R R Yager (2009). On the fusion of non-independent belief structures. International Journal General Systems, 38(5), 505--531.
[20]
X Su, P Xu, S Mahadevan and Y Deng (2014). On consideration of dependence and reliability of evidence in Dempster-Shafer theory[J]. Journal Information Compute. Science, 11(14), 4901--4910.
[21]
P Smets and R Kennes (1994). The transferable belief model[J]. Artificial Intelligence, 66, 191--234.
[22]
R N Forthofer and R G Lehnen (1981). Rank Correlation Methods. In: Public Program Analysis, Springer, Boston, 146--163.
[23]
UCI Machine Learning Repository (1988). Iris Data Set. [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Iris.
[24]
P Xu, Y Deng, X Su and S Mahadevan (2013). A new method to determine basic probability assignment from training data[J]. Knowledge Based System, 46(1), 69--80.

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    CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering
    October 2020
    1038 pages
    ISBN:9781450377720
    DOI:10.1145/3424978
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    Published: 20 October 2020

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    Author Tags

    1. D-S evidence theory
    2. Dependence evidence
    3. Kendall correlation coefficient

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    CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;
    Overall Acceptance Rate 368 of 770 submissions, 48%

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