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A novel method for combining conflicting evidences based on information entropy

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Abstract

Dempster-Shafer evidence theory is widely used to deal with uncertainty in intelligent systems. However, the application of this theory is constrained by the failure to balance multiple conflict evidence. The existing studies have primarily focused on investigating similarity of evidence. However, the similarity measurement is highly dependent on the capability of distance functions and will substantially increase the computational complexity. So, the efficient method with acceptable expense should be intensively investigated. In this paper, we propose a new method based on the variance of information entropy to handle the conflict of evidence. First, the fuzzy preference relations based on the variance of information entropy are constructed for multiple pieces of evidence. Next, credible values of alternative evidence are calculated. Finally, according to the Dempster’s rule of combination, the weighted average combination result can be obtained. Typical example and several actual data are used to demonstrate that the proposed method is more reasonable than some existing methods both in managing conflict and reducing computational complexity.

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References

  1. Altınçay H (2006) On the independence requirement in dempster-shafer theory for combining classifiers providing statistical evidence. Appl Intell 25(1):73–90

    Article  MATH  Google Scholar 

  2. Clausius R (1867) The mechanical theory of heat: with its applications to the steam-engine and to the physical properties of bodies. J. van Voorst

  3. Dempster AP (1967) Upper and lower probabilities induced by a multivariate mapping. Ann Math Stat 38:325–339

    Article  MATH  Google Scholar 

  4. Deng X, Han D, Dezert J, Deng Y, Shyr Y (2016) Evidence combination from an evolutionary game theory perspective. IEEE Trans Cybern 46(9):2070–2082

    Article  Google Scholar 

  5. Deng X, Hu Y, Deng Y, Mahadevan S (2014) Environmental impact assessment based on D numbers. Expert Syst Appl 41(2):635–643

    Article  Google Scholar 

  6. Deng X, Lu X, Chan FT, Sadiq R, Mahadevan S, Deng Y (2015) D-CFPR: D numbers extended consistent fuzzy preference relations. Knowl-Based Syst 73:61–68

    Article  Google Scholar 

  7. Deng Y (2015) Generalized evidence theory. Appl Intell 43(3):530–543

    Article  Google Scholar 

  8. Deng Y (2016) Deng entropy. Chaos, Solitons Fractals 91:549–553

    Article  Google Scholar 

  9. Deng Y (2017) Fuzzy analytical hierarchy process based on canonical representation on fuzzy numbers. J Comput Anal Appl 22(2):201–228

    Google Scholar 

  10. Deng Y, Shi WK, Zhu ZZ, Liu Q (2004) Combining belief functions based on distance of evidence. Decis Support Syst 38(3):489–493

    Article  Google Scholar 

  11. Denoeux T (1995) A k-nearest neighbor classification rule based on dempster-shafer theory. IEEE Trans Syst Man Cybern 25(5):804–813

    Article  Google Scholar 

  12. Denoeux T (2008) Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif Intell 172(2-3):234–264

    Article  MathSciNet  MATH  Google Scholar 

  13. Du W, Gao Y, Liu C, Zheng Z, Wang Z (2015) Adequate is better: particle swarm optimization with limited-information. Appl Math Comput 268:832–838

    Article  MathSciNet  Google Scholar 

  14. Du WB, Ying W, Yan G, Zhu YB, Cao XB (2016) Heterogeneous strategy particle swarm optimization. IEEE Transactions on Circuits and Systems II: Express Briefs. doi:10.1109/TCSII.2016.2595597. In press

    Google Scholar 

  15. Du WB, Zhou XL, Lordan O, Wang Z, Zhao C, Zhu YB (2016) Analysis of the chinese airline network as multi-layer networks. Transportation Research Part E: Logistics and Transportation Review 89:108–116

    Article  Google Scholar 

  16. Dubois D, Prade H (1986) On the unicity of dempster rule of combination. Int J Intell Syst 1(2):133–142

    Article  MATH  Google Scholar 

  17. Dubois D, Prade H (1988) Representation and combination of uncertainty with belief functions and possibility measures. Comput Intell 4(3):244–264

    Article  Google Scholar 

  18. Florea M, Jousselme A, Bosse E (2009) Robust combination rules for evidence theory. Information Fusion 10(2):183– 197

    Article  Google Scholar 

  19. Fu C, Yang JB, Yang SL (2015) A group evidential reasoning approach based on expert reliability. Eur J Oper Res 246(3):886–893

    Article  MathSciNet  MATH  Google Scholar 

  20. Haenni R (2002) Are alternatives to dempster’s rule of combination real alternatives?: Comments on about the belief function combination and the conflict management problem—-lefevre et al. Information Fusion 3(3):237–239

    Article  Google Scholar 

  21. Haenni R (2005) Shedding new light on zadeh’s criticism of dempster’s rule of combination. In: 2005 7Th International conference on information fusion, vol 2. IEEE, p 6

  22. Huynh V, Nakamori Y, Ho T, Murai T (2006) Multiple-attribute decision making under uncertainty: The evidential reasoning approach revisited. IEEE Trans Syst Man Cybern Syst Hum 36(4):804–822

    Article  Google Scholar 

  23. Jiang W, Luo Y, Qin X, Zhan J (2015) An improved method to rank generalized fuzzy numbers with different left heights and right heights. J Intell Fuzzy Syst 28(5):2343–2355

    Article  MathSciNet  MATH  Google Scholar 

  24. Jiang W, Wei B, Qin X, Zhan J, Tang Y (2016) Sensor Data Fusion Based on a New Conflict Measure. Math Probl Eng. 2016, Article ID 5769061, 11 pages

  25. Jiang W, Wei B, Xie C, Zhou D (2016) An evidential sensor fusion method in fault diagnosis. Adv Mech Eng 8(3):1–7

    Article  Google Scholar 

  26. Jiang W, Xie C, Wei B, Zhou D (2016) A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades. Adv Mech Eng 8(4):1–16

    Article  Google Scholar 

  27. Jiang W, Xie C, Zhuang M, Shou Y, Tang Y (2016) Sensor data fusion with z-numbers and its application in fault diagnosis. Sensors 16(9):1509. doi:10.3390/s16091509

    Article  Google Scholar 

  28. Jiang W, Zhan J, Zhou D, Li X (2016) A method to determine generalized basic probability assignment in the open world. Math Probl Eng. Article ID 3878634. doi:10.1155/2016/3878634

  29. Jousselme AL, Grenier D, Bosse E (2001) A new distance between two bodies of evidence. Information Fusion 2(2):91– 101

    Article  Google Scholar 

  30. Lebowitz JL (1993) Boltzmann’s entropy and time’s arrow. Phys Today 46:32–32

    Article  Google Scholar 

  31. Lee LW (2012) Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency. Expert Syst Appl 39(14):11,666– 11,676

    Article  Google Scholar 

  32. Lefèvre E., Colot O, Vannoorenberghe P (2002) Belief function combination and conflict management. Information Fusion 3(2):149–162

    Article  Google Scholar 

  33. Lefèvre E., Elouedi Z (2013) How to preserve the conflict as an alarm in the combination of belief functions? Decis Support Syst 56:326–333

    Article  Google Scholar 

  34. Liu W (2006) Analyzing the degree of conflict among belief functions. Artificial Intelligence 170(11):909–924

    Article  MathSciNet  MATH  Google Scholar 

  35. Liu Z.g., Pan Q, Dezert J, Martin A (2016) Adaptive imputation of missing values for incomplete pattern classification. Pattern Recogn 52:85–95

    Article  Google Scholar 

  36. Liu Z.g., Pan Q, Dezert J, Mercier G (2015) Credal c-means clustering method based on belief functions. Knowl-Based Syst 74:119–132

    Article  Google Scholar 

  37. Ma J, Liu W, Miller P, Zhou H (2016) An evidential fusion approach for gender profiling. Inf Sci 333:10–20

    Article  Google Scholar 

  38. Murphy C (2000) Combining belief functions when evidence conflicts. Decis Support Syst 29(1):1–9

    Article  MathSciNet  Google Scholar 

  39. Ning X, Yuan J, Yue X (2016) Uncertainty-based optimization algorithms in designing fractionated spacecraft. Scientific Reports 6:22,979

    Article  Google Scholar 

  40. Ning X, Yuan J, Yue X, Ramirez-Serrano A (2014) Induced generalized choquet aggregating operators with linguistic information and their application to multiple attribute decision making based on the intelligent computing. J Intell Fuzzy Syst 27(3):1077– 1085

    MathSciNet  MATH  Google Scholar 

  41. Ning X, Zhang T, Wu Y, Zhang P, Zhang J, Li S, Yue X, Yuan J (2016) Coordinated parameter identification technique for the inertial parameters of non-cooperative target. PloS one 11(4):e0153,604

    Article  Google Scholar 

  42. Pichon F, Denœux T. (2010) The unnorMalized dempster’s rule of combination: a new justification from the least commitment principle and some extensions. J Autom Reason 45(1):61– 87

    Article  MathSciNet  MATH  Google Scholar 

  43. Fisher RA Iris-dataset. http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

  44. Shafer G (1976) A mathematical theory of evidence, vol 1. Princeton university press, Princeton

  45. Shafer G (2011) A betting interpretation for probabilities and dempster–shafer degrees of belief. Int J Approx Reason 52(2):127–136

    Article  MathSciNet  MATH  Google Scholar 

  46. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1):3–55

    Article  MathSciNet  Google Scholar 

  47. Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66(2):191–234

    Article  MathSciNet  MATH  Google Scholar 

  48. Tanino T (1984) Fuzzy preference orderings in group decision making. Fuzzy Sets Syst 12(84):117–131

    Article  MathSciNet  MATH  Google Scholar 

  49. Yager RR (1987) On the dempster-shafer framework and new combination rules. Inf Sci 41(2):93–137

    Article  MathSciNet  MATH  Google Scholar 

  50. Yager RR (2004) Decision making using minimization of regret. Int J Approx Reason 36(2):109–128

    Article  MathSciNet  Google Scholar 

  51. Yang JB, Xu DL (2013) Evidential reasoning rule for evidence combination. Artif Intell 205:1–29

    Article  MathSciNet  MATH  Google Scholar 

  52. Yang Y, Han D (2016) A new distance-based total uncertainty measure in the theory of belief functions. Knowl-Based Syst 94:114–123

    Article  Google Scholar 

  53. Yang Y, Han D, Han C (2013) Discounted combination of unreliable evidence using degree of disagreement. Int J Approx Reason 54(8):1197–1216

    Article  MATH  Google Scholar 

  54. Zadeh L (1986) A simple view of the dempster-shafer theory of evidence and its implication for the rule of combination. AI Mag 7(2):85

    Google Scholar 

  55. Zadeh LA (1983) The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst 11(83):197–198

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The work is partially supported by National High Technology Research and Development Program of China (863 Program) (Grant No. 2013AA013801), National Natural Science Foundation of China (Grant Nos. 61174022,61573290,61503237), China State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No.BUAA-VR-14KF-02).

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Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Jin Qian, Xingfeng Guo & Yong Deng

  2. Institute of Integrated Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China

    Yong Deng

  3. Big Data Decision Institute, Jinan University, Tianhe, Guangzhou, 510632, China

    Yong Deng

  4. School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Yong Deng

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  1. Jin Qian
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  2. Xingfeng Guo
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  3. Yong Deng
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Correspondence to Yong Deng.

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Qian, J., Guo, X. & Deng, Y. A novel method for combining conflicting evidences based on information entropy. Appl Intell 46, 876–888 (2017). https://doi.org/10.1007/s10489-016-0875-y

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