A New Belief Entropy in Dempster–Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment

Dempster–Shafer theory has been widely used in many applications, especially in the measurement of information uncertainty. However, under the D-S theory, how to use the belief entropy to measure the uncertainty is still an open issue. In this paper, we list some significant properties. The main contribution of this paper is to propose a new entropy, for which some properties are discussed. Our new model has two components. The first is Nguyen entropy. The second component is the product of the cardinality of the frame of discernment (FOD) and Dubois entropy. In addition, under certain conditions, the new belief entropy can be transformed into Shannon entropy. Compared with the others, the new entropy considers the impact of FOD. Through some numerical examples and simulation, the proposed belief entropy is proven to be able to measure uncertainty accurately.

[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.