Conflicting evidence combination from the perspective of networks

Abstract Dempster-Shafer evidence theory is widely used in the field of information fusion especially when confronting with uncertainties. However, Dempster’s rule of combination may lead to counter-intuitive results when dealing with highly conflicting bodies of evidence (BOEs). Numerous methods were proposed to address this problem. Enlightened by the research of interaction among nodes in complex networks, this paper study the combination of evidences from the perspective of networks: BOEs are regarded as nodes, the conflicting degree between BOEs is considered as one possible interaction between nodes. The direct and indirect interactions among nodes in networks are considered together to determine the weights of the BOEs. After process of weighted average, the modified BOEs can be efficiently combined by Dempster’s rule of combination. A numerical example is illustrated to show the use and better performance of the proposed method.

[1]  Justin G. Hollands,et al.  Target Detection and Identification Performance Using an Automatic Target Detection System , 2017, Hum. Factors.

[2]  Fabio Cuzzolin,et al.  A Geometric Approach to the Theory of Evidence , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Shi Wen-kang,et al.  Combining belief functions based on distance of evidence , 2004 .

[4]  Yong DENG,et al.  Uncertainty measure in evidence theory , 2020, Science China Information Sciences.

[5]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Éloi Bossé,et al.  A new distance between two bodies of evidence , 2001, Inf. Fusion.

[7]  Yong Deng,et al.  Vital Spreaders Identification in Complex Networks with Multi-Local Dimension , 2019, Knowl. Based Syst..

[8]  R. Yager On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[9]  Yong Deng Information Volume of Mass Function , 2020, Int. J. Comput. Commun. Control.

[10]  Marcelo L. O. Souza,et al.  Preliminary Analysis of Solar Cell Interconnections Welding Parameters Using Design of Experiments for Future Optimization , 2020 .

[11]  Filipi N. Silva,et al.  A comparative analysis of knowledge acquisition performance in complex networks , 2021, Inf. Sci..

[12]  Yong Deng,et al.  Pignistic Belief Transform: A New Method of Conflict Measurement , 2020, IEEE Access.

[13]  Chongzhao Han,et al.  Weighted evidence combination based on distance of evidence and uncertainty measure: Weighted evidence combination based on distance of evidence and uncertainty measure , 2012 .

[14]  Yu Luo,et al.  Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers , 2015, Int. J. Comput. Commun. Control.

[15]  Catherine K. Murphy Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[16]  Yong Deng,et al.  Alternatives selection for produced water management: A network-based methodology , 2020, Eng. Appl. Artif. Intell..

[17]  Rolf Haenni,et al.  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 , 2002, Inf. Fusion.

[18]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[19]  Yu Luo,et al.  An improved method to rank generalized fuzzy numbers with different left heights and right heights , 2015, J. Intell. Fuzzy Syst..

[20]  Eric Lefevre,et al.  Belief function combination and conflict management , 2002, Inf. Fusion.

[21]  Jin Qi,et al.  Information-intensive design solution evaluator combined with multiple design and preference information in product design , 2021, Inf. Sci..

[22]  Jean Dezert,et al.  On the Validity of Dempster's Fusion Rule and its Interpretation as a Generalization of Bayesian Fusion Rule , 2014, Int. J. Intell. Syst..

[23]  Han De,et al.  Weighted evidence combination based on distance of evidence and uncertainty measure , 2011 .

[24]  Jiao-Jiao Zhong,et al.  Generalized combination rule for evidential reasoning approach and Dempster-Shafer theory of evidence , 2021, Inf. Sci..

[25]  Zied Elouedi,et al.  How to preserve the conflict as an alarm in the combination of belief functions? , 2013, Decis. Support Syst..

[26]  S. Mahadevan,et al.  Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[27]  Xiaojian Xu,et al.  Correlation-oriented complex system structural risk assessment using Copula and belief rule base , 2021, Inf. Sci..

[28]  Lotfi A. Zadeh,et al.  A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..

[29]  Yong Deng,et al.  Generalized Belief Entropy and Its Application in Identifying Conflict Evidence , 2019, IEEE Access.

[30]  Weiru Liu,et al.  Reinvestigating Dempster's Idea on Evidence Combination , 2000, Knowledge and Information Systems.

[31]  S. Strogatz Exploring complex networks , 2001, Nature.

[32]  Henri Prade,et al.  Representation and combination of uncertainty with belief functions and possibility measures , 1988, Comput. Intell..

[33]  Jixiang Deng,et al.  Information Volume of Fuzzy Membership Function , 2021, Int. J. Comput. Commun. Control.

[34]  Yong Deng,et al.  The vulnerability of communities in complex network: An entropy approach , 2019, Reliab. Eng. Syst. Saf..

[35]  Li Fu,et al.  A Novel Fuzzy Approach for Combining Uncertain Conflict Evidences in the Dempster-Shafer Theory , 2019, IEEE Access.

[36]  Fuyuan Xiao,et al.  A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion , 2020, Inf. Sci..

[37]  Liguo Fei,et al.  Multi-criteria decision making in Pythagorean fuzzy environment , 2019, Applied Intelligence.