A New Belief Entropy in Dempster–Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment
暂无分享,去创建一个
Qian Pan | Jiapeng Li | Qian Pan | Jiapeng Li
[1] Fuyuan Xiao,et al. Conflict management based on belief function entropy in sensor fusion , 2016, SpringerPlus.
[2] Philippe Smets,et al. Constructing the Pignistic Probability Function in a Context of Uncertainty , 1989, UAI.
[3] Ahmed Frikha,et al. Analytic hierarchy process for multi-sensor data fusion based on belief function theory , 2015, Eur. J. Oper. Res..
[4] Ronald R. Yager,et al. Arithmetic and Other Operations on Dempster-Shafer Structures , 1986, Int. J. Man Mach. Stud..
[5] R. Hartley. Transmission of information , 1928 .
[6] Yongchuan Tang,et al. A modified belief entropy in Dempster-Shafer framework , 2017, PloS one.
[7] Yi Yang,et al. A new distance-based total uncertainty measure in the theory of belief functions , 2016, Knowl. Based Syst..
[8] Naif Alajlan,et al. Decision Making with Ordinal Payoffs Under Dempster–Shafer Type Uncertainty , 2013, Int. J. Intell. Syst..
[9] Robert LIN,et al. NOTE ON FUZZY SETS , 2014 .
[10] S. Moral,et al. MEASURES OF ENTROPY IN THE THEORY OF EVIDENCE , 1988 .
[11] Yong Deng,et al. Generalized evidence theory , 2014, Applied Intelligence.
[12] Ronald R. Yager,et al. Entropy and Specificity in a Mathematical Theory of Evidence , 2008, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[13] Limao Zhang,et al. Improved Fuzzy Bayesian Network-Based Risk Analysis With Interval-Valued Fuzzy Sets and D–S Evidence Theory , 2020, IEEE Transactions on Fuzzy Systems.
[14] Shuai Xu,et al. An improved belief entropy–based uncertainty management approach for sensor data fusion , 2017, Int. J. Distributed Sens. Networks.
[15] G. Klir,et al. Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .
[16] Xinyang Deng,et al. Evaluating Green Supply Chain Management Practices Under Fuzzy Environment: A Novel Method Based on D Number Theory , 2019, Int. J. Fuzzy Syst..
[17] Ronald R. Yager,et al. Classic Works of the Dempster-Shafer Theory of Belief Functions , 2010, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[18] Jean Dezert,et al. Credal c-means clustering method based on belief functions , 2015, Knowl. Based Syst..
[19] James C. Bezdek,et al. Uncertainty measures for evidential reasoning II: A new measure of total uncertainty , 1993, Int. J. Approx. Reason..
[20] Mohammad R. Akbarzadeh-Totonchi,et al. A qualified description of extended fuzzy logic , 2013, Inf. Sci..
[21] Weiru Liu,et al. An evidential fusion approach for gender profiling , 2016, Inf. Sci..
[22] Prakash P. Shenoy,et al. A new definition of entropy of belief functions in the Dempster-Shafer theory , 2018, Int. J. Approx. Reason..
[23] Quan Pan,et al. A new belief-based K-nearest neighbor classification method , 2013, Pattern Recognit..
[24] Wen Jiang,et al. An evidential sensor fusion method in fault diagnosis , 2016 .
[25] Yong Deng,et al. Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory , 2019, Int. J. Comput. Commun. Control.
[26] Yafei Song,et al. A new approach to construct similarity measure for intuitionistic fuzzy sets , 2019, Soft Comput..
[27] Yong Deng,et al. Performer selection in Human Reliability analysis: D numbers approach , 2019, Int. J. Comput. Commun. Control.
[28] George J. Klir,et al. Fuzzy sets, uncertainty and information , 1988 .
[29] Yong Deng,et al. Risk Evaluation in Failure Mode and Effects Analysis Based on Dempster-Shafer Theory and Prospect Theory ⋆ , 2014 .
[30] Sankaran Mahadevan,et al. Reliability analysis with linguistic data: An evidential network approach , 2017, Reliab. Eng. Syst. Saf..
[31] Ying-Ming Wang,et al. A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks , 2007, Expert Syst. Appl..
[32] 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.
[33] Yong Deng,et al. Intuitionistic Evidence Sets , 2018, IEEE Access.
[34] Yong Deng,et al. An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades , 2012 .
[35] Quan Pan,et al. Adaptive imputation of missing values for incomplete pattern classification , 2016, Pattern Recognit..
[36] Yi Yang,et al. A novel approach to pre-extracting support vectors based on the theory of belief functions , 2016, Knowl. Based Syst..
[37] Alireza Mohammad Shahri,et al. Uncertainty evaluation for a Dezert–Smarandache theory-based localization problem , 2014, Int. J. Gen. Syst..
[38] Thierry Denoeux,et al. A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..
[39] Yang Liu,et al. An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP , 2015 .
[40] Qing Liu,et al. Derive knowledge of Z-number from the perspective of Dempster-Shafer evidence theory , 2019, Eng. Appl. Artif. Intell..
[41] Feller William,et al. An Introduction To Probability Theory And Its Applications , 1950 .
[42] Jian-Bo Yang,et al. A group evidential reasoning approach based on expert reliability , 2015, Eur. J. Oper. Res..
[43] Yong Deng,et al. A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function , 2018, Entropy.
[44] Yong Deng. D Numbers: Theory and Applications ? , 2012 .
[45] Éloi Bossé,et al. Measuring ambiguity in the evidence theory , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[46] George J. Klir,et al. Uncertainty in the dempster-shafer Theory - A Critical Re-examination , 1990 .
[47] Fuyuan Xiao,et al. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory , 2016, Sensors.
[48] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[49] Theresa Beaubouef,et al. Rough Sets , 2019, Lecture Notes in Computer Science.
[50] Chunhe Xie,et al. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis , 2016, Sensors.
[51] Muharrem Dügenci,et al. A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information , 2016, Appl. Soft Comput..
[52] Mohammad R. Akbarzadeh-Totonchi,et al. Introducing validity in fuzzy probability for judicial decision-making , 2014, Int. J. Approx. Reason..
[53] D. Dubois,et al. Properties of measures of information in evidence and possibility theories , 1987 .
[54] Dan Wang,et al. A New Belief Entropy Based on Deng Entropy , 2019, Entropy.
[55] Joaquín Abellán,et al. Analyzing properties of Deng entropy in the theory of evidence , 2017 .
[56] Ronald R. Yager,et al. Pythagorean Membership Grades, Complex Numbers, and Decision Making , 2013, Int. J. Intell. Syst..
[57] N. Pal,et al. QUANTIFICATION OF CONFLICT IN DEMPSTER-SHAFER FRAMEWORK: A NEW APPROACH , 1996 .
[58] Wen Jiang,et al. A Novel Z-Network Model Based on Bayesian Network and Z-Number , 2020, IEEE Transactions on Fuzzy Systems.
[59] José M. Merigó,et al. Induced aggregation operators in decision making with the Dempster‐Shafer belief structure , 2009, Int. J. Intell. Syst..
[60] Quan Pan,et al. Credal classification rule for uncertain data based on belief functions , 2014, Pattern Recognit..
[61] James C. Bezdek,et al. Uncertainty measures for evidential reasoning I: A review , 1992, Int. J. Approx. Reason..
[62] Yong Deng,et al. Divergence Measure of Belief Function and Its Application in Data Fusion , 2019, IEEE Access.
[63] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.