L. A. Zadeh, Fuzzy sets, Inf. Control, vol. 8, no. 3, pp. 338-353, 1965.
H. Friedman, The consistency of classical set theory relative to a set theory with intuitionistic logic, J. Symb. Log., vol. 38, no. 2, pp. 315-319, 1973.
D. Dubois, Possibility theory and statistical reasoning, Comput. Stat. Data Anal., vol. 51, no. 1, pp. 47-69, 2006.
A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, Ann. Math. Stat., vol. 38, no. 2, pp. 325-339, 1967.
G. Shafer, A Mathematical Theory of Evidence. Princeton, NJ, USA: Princeton University Press, 1976.
D. Molodtsov, Soft set theory: First results, Comp. Math. Appl., vol. 37, nos. 4&5, pp. 19-31, 1999.
P. K. Maji, R. Biswas, and A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., vol. 9, no. 3, pp. 589-602, 2001.
P. Singh and G. Dhiman, A fuzzy-LP approach in time series forecasting, presented at Int. Conf. Pattern Recognition and Machine Intelligence, Kolkata, India, 2017, pp. 243-253.
P. Singh, K. Rabadiya, and G. Dhiman, A four-way decision-making system for the Indian summer monsoon rainfall, Mod. Phys. Lett. B, vol. 32, no. 25, p. 1850304, 2018.
G. Dhiman and V. Kumar, Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Softw., vol. 114, pp. 48-70, 2017.
P. Singh and G. Dhiman, A hybrid fuzzy time series forecasting model based on granular computing and bio- inspired optimization approaches, J. Comput. Sci., vol. 27, pp. 370-385, 2018.
P. Singh and G. Dhiman, Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs), Appl. Soft Comput., vol. 72, pp. 121-139, 2018.
G. Dhiman and A. Kaur, A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization, presented at Soft Computing for Problem Solving, Singapore, 2019, pp. 599-615.
D. Yong, Deng entropy, Chao Solitons Fractals, vol. 91, pp. 549-553, 2016.
D. Wang, J. L. Gao, and D. J. Wei, A new belief entropy based on Deng entropy, Entropy, vol. 21, no. 10, p. 987, 2019.
U. Hohle, Entropy with respect to plausibility measures, in Proc. 12th IEEE Int. Symp. Multiple Valued Logic, Paris, France, 1982.
D. Dubois and H. Prade, Properties of measures of information in evidence and possibility theories, Fuzzy Sets Syst., vol. 24, no. 2, pp. 161-182, 1987.
L. P. Pan and Y. Deng, A new belief entropy to measure uncertainty of basic probability assignments based on belief function and plausibility function, Entropy, vol. 20, no. 11, p. 842, 2018.
J. C. Hou, Grey relational analysis method for multiple attribute decision making in intuitionistic fuzzy setting, J. Conv. Inf. Technol., vol. 5, no. 10, pp. 194-199, 2010.
Z. W. Li, G. Q. Wen, and N. X. Xie, An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster-Shafer theory of evidence: An application in medical diagnosis, Artif. Intell. Med., vol. 64, no. 3, pp. 161-171, 2015.
R. Belohlavek, Systems, uncertainty, and information: A legacy of George J. Klir, Int. J. Gen. Syst., vol. 46, no. 8, pp. 792-823, 2017.
J. Kacprzyk, D. Filev, and G. Beliakov, Granular, Soft and Fuzzy Approaches for Intelligent Systems. Springer, 2017.
C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J., vol. 27, no. 3, pp. 379-423, 1948.
J. W. Wang, Y. Hu, F. Y. Xiao, X. Y. Deng, and Y. Deng, A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis, Artif. Intell. Med., vol. 69, pp. 1-11, 2016.
F. Y. Xiao, A hybrid fuzzy soft sets decision making method in medical diagnosis, IEEE Access, vol. 6, pp. 25 300-25 312, 2018.
X. D. Wang and Y. F. Song, Uncertainty measure in evidence theory with its applications, Appl. Intell., vol. 48, no. 7, pp. 1672-1688, 2018.
P. Dutta, Modeling of variability and uncertainty in human health risk assessment, MethodsX, vol. 4, pp. 76-85, 2017.
W. Jiang, B. Y. Wei, C. H. Xie, and D. Y. Zhou, An evidential sensor fusion method in fault diagnosis, Adv. Mech. Eng., vol. 8, no. 3, pp. 1-7, 2016.
H. H. Xu and Y. Deng, Dependent evidence combination based on decision-making trial and evaluation laboratory method, Int. J. Intell. Syst., vol. 34, no. 7, pp. 1555-1571, 2019.
F. Cuzzolin, A geometric approach to the theory of evidence, IEEE Trans. Syst. Man Cybernet. Part C Appl. Rev., vol. 38, no. 4, pp. 522-534, 2008.
H. Seiti and A. Hafezalkotob, Developing pessimistic-optimistic risk-based methods for multi-sensor fusion: An interval-valued evidence theory approach, Appl. Soft Comput., vol. 72, pp. 609-623, 2018.
Y. T. Liu, N. R. Pal, A. R. Marathe, and C. T. Lin, Weighted fuzzy Dempster-Shafer framework for multimodal information integration, IEEE Trans. Fuzzy Syst., vol. 26, no. 1, pp. 338-352, 2018.
T. Tanino, Fuzzy preference orderings in group decision making, Fuzzy Sets Syst., vol. 12, no. 2, pp. 117-131, 1984.
L. W. Lee, Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency, Expert Syst. Appl., vol. 39, no. 14, pp. 11 666-11 676, 2012.