Abstract
Dempster-Shafer evidence theory is an efficient tool used in knowledge reasoning and decision-making under uncertain environments. Conflict management is an open issue in Dempster-Shafer evidence theory. There is no good practice that can be generally accepted until the presence of generalized evidence theory (GET). GET addresses conflict management in an open world, where the frame of discernment (FOD) is incomplete since uncertainty and lacking knowledge. With the in-depth study, however, the original generalized combination rule (GCR) still has its issue. As an example, based on the original GCR, the system judges whether the FOD is complete or not even though the GBPAs clearly indicate that the proposition is outside of FOD. In this paper, we proposed a modified generalized combination rule (mGCR) in the framework of GET. The mGCR satisfies all properties of GCR in GET, illustrating and modeling the real world more reasonably than the original. Numerical examples demonstrate that mGCR combines GBPAs effectively and has more distinct geometric and physical meaning than the original GCR. Several experiments using real data sets are presented at the end of this paper to evaluate the effectiveness of mGCR.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
Shafer performed the method in Chapter III of his famous monograph A mathematical theory of evidence[30]
UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/Iris
UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/seeds#
References
Bagheri E, Zafarani R, Ebrahimi M (2009) Can reputation migrate? on the propagation of reputation in multi-context communities. Knowl-Based Syst 22(6):410–420
Chin KS, Fu C (2015) Weighted cautious conjunctive rule for belief functions combination. Inf Sci 325:70–86
Cuzzolin F (2008) A geometric approach to the theory of evidence. IEEE Trans Syst Man Cybern Part C: Appl Rev 38(4):522–534
Dempster A (1967) Upper and lower probabilities induced by multivalued mapping. Ann Math Stat 38 (2):325–339
Deng X, Hu Y, Deng Y, Mahadevan S (2014) Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl 41(1):156–167
Deng Y (2015) Generalized evidence theory. Appl Intell 43(3):530–543
Deng Y (2016) Deng entropy. Chaos, Solitons Fractals 91:549–553
Deng Y (2017) Fuzzy analytical hierarchy process based on canonical representation on fuzzy numbers. J Comput Anal Appl 22(2):201–228
Deng Y, Liu Y, Zhou D (2015) An improved genetic algorithm with initial population strategy for symmetric TSP. Math Problems Eng 2015:212,794. doi:10.1155/2015/212794
Dubios D, Prade H (1994) A survey of belief revision and updating rules in various uncertainty models. Int J Intell Syst 9(1):61–100
Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Hum Genet 7(2):179–188
Fu C, Chin KS (2014) Robust evidential reasoning approach with unknown attribute weights. Knowl-Based Syst 59(2):9–20
Fu C, Yang S (2012) An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements. Eur J Oper Res 223(1):167–176
JiangW,Wei B, Qin X, Zhan J, Tang Y (2016a) Sensor data fusion based on a new conflict measure. Math Probl Eng 2016, Article ID 5769061:11 pages, doi:10.1155/2016/5769061
Jiang W, Wei B, Xie C, Zhou D (2016b) An evidential sensor fusion method in fault diagnosis. Adv Mech Eng 8(3):1–7. doi:10.1177/1687814016641820
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. doi:10.1177/1687814016644579
Jiang W, Zhan J, Zhou D, Li X (2016d) A method to determine generalized basic probability assignment in the open world. Math Probl Eng 2016, Article ID 3878634:11 pages, doi:10.1155/2016/3878634
Jiang W, Zhuang M, Qin X, Tang Y (2016e) Conflicting evidence combination based on uncertainty measure and distance of evidence. SpringerPlus 5(1):1–11. doi:10.1186/s40064-016-2863-4
Kang B, Deng Y, Sadiq R, Mahadevan S (2012) Evidential cognitive maps. Knowl-Based Syst 35:77–86
Li M, Lu X, Zhang Q, Deng Y (2014) Multiscale probability transformation of basic probability assignment. Math Probl Eng 2014, doi:10.1155/2014/319264
Liu HC, You JX, Fan XJ, Lin QL (2014a) Failure mode and effects analysis using d numbers and grey relational projection method. Expert Syst Appl 41(10):4670–4679
Liu W (2006) Analyzing the degree of conflict among belief functions. Artif Intell 170(11):909–924
Liu YZ, Jiang YC, Liu X, Yang SL (2008) A combination strategy for multiclass classification based on multiple association rules. Knowl-Based Syst 21(8):786–793
Liu ZG, Pan Q, Dezert J (2014a) A belief classification rule for imprecise data. Appl Intell 40(2):214–228. doi:10.1007/s10489-013-0453-5
Lolli F, Ishizaka A, Gamberini R, Rimini B, Messori M (2015) Flowsort-gdss -a novel group multi-criteria decision support system for sorting problems with application to fmea. Expert Syst Appl 42:6342–6349
Ma J, Liu W, Miller P, Zhou H (2016) An evidential fusion approach for gender profiling. Inf Sci 333:10–20
Niu D,Wei Y, Shi Y, Karimi HR (2012) A novel evaluation model for hybrid power system based on vague set and dempster-shafer evidence theory. Math Probl Eng doi:10.1155/2012/784389
Rikhtegar N, Mansouri N, Oroumieh AA, Yazdani-Chamzini A, Zavadskas EK, Kildien? S (2014) Environmental impact assessment based on group decision-making methods in mining projects. Econ Res 27(1):378–392
RYager R, Alajlan N (2013) Decision making with ordinal payoffs under dempster-shafer type uncertainty. Int J Intell Syst 28(11):1039–1053
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, New Jersey
Shafer G (2015) Dempster’s rule of combination. Int J Approx Reason doi:10.1016/j.ijar.2015.12.009
Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66(2):191–234
Su X, Mahadevan S, Han W, Deng Y (2015a) Combining dependent bodies of evidence. Appl Intell doi:10.1007/s10489-015-0723-5
Su X, Mahadevan S, Xu P, Deng Y (2015b) Dependence assessment in Human Reliability Analysis using evidence theory and AHP. Risk Anal 35:1296–1316
Tang Y, Zhou D, Jiang W (2016) A new fuzzy-evidential controller for stabilization of the planar inverted pendulum system. PloS ONE 11(8):e0160,416. doi:10.1371/journal.pone.0160416
Wang P (2008) The reliable combination rule of evidence in Dempster-Shafer theory. Proc-1st Int Congress Image Signal Process, CISP 2008 2:166–170. doi:10.1109/CISP.2008.602
Xu PD, Su XY, Mahadevan S, Li CZ, Deng Y (2014) A non-parametric method to determine basic probability assignment for classification problems. Appl Intell 41:681–693
Yager RR (1987) On the dempster-shafer framework and new combination rules. Inf Sci 41(2):93 – 137. doi:10.1016/0020-0255(87)90007-7
Yang J, Xu D (2013) Evidential reasoning rule for evidence combination. Artif Intell 205:1–29
Yang Y, Han D (2016) A new distance-based total uncertainty measure in the theory of belief functions. Knowl-Based Syst 94:114–123
Zadeh L (1986) A simple view of the dempter-shafer theory of evidence and its implication for the rule of combination. AI Mag 7(1):34–38
Zhao X, Wang R, Gu H, Song G, Mo Y (2014) Innovative data fusion enabled structural health monitoring approach. Math Probl Eng 2014,. doi:http://dx.doi.org/10.1155/2014/369540
Acknowledgments
We greatly appreciate the editor’s encouragement and the anonymous reviewers’ valuable comments and suggestions to improve this paper. The work is partially supported by National Natural Science Foundation of China (Grant No. 61671384), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016JM6018), the Fund of SAST (Program No. SAST2016083), the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (Program No. Z2016122).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jiang, W., Zhan, J. A modified combination rule in generalized evidence theory. Appl Intell 46, 630–640 (2017). https://doi.org/10.1007/s10489-016-0851-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-016-0851-6