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Research on Two-Stage Hesitate Fuzzy Information Fusion Framework Incorporating Prospect Theory and Dichotomy Algorithm

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Abstract

In order to control the systematic divergence among decision makers (DMs) and preserve the original decision preference, this paper proposes a novel decision information fusion framework under the hesitant fuzzy environment. First, a maximum compactness-based normalization method is presented to normalize hesitant fuzzy elements (HFEs) as pretreatment of decision data. Second, prospect theory is introduced to assign the optimal aggregation weights to maximize the efficiency of the preference aggregation process, in which the expected consensus threshold is viewed as a reference point estimated through statistic inference to distinguish DMs’ status. Third, an effective feedback mechanism is designed to improve group consensus, and the dichotomy algorithm is utilized to search optimal feedback weight to preserve original decision information. Finally, a case study and comparison analysis are illustrated to show the efficiency of the proposed hesitant fuzzy information fusion method.

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References

  1. Antonio, S., Humberto, B.: A wrapper methodology to learn interval-valued fuzzy rule-based classification systems. Appl. Soft Comput. 104, 107249 (2021)

    Article  Google Scholar 

  2. Rong, Y., Liu, Y., Pei, Z.: Interval-valued intuitionistic fuzzy generalized bonferroni mean operators for multi-attribute decision making. Int. J. Fuzzy Syst. 4, 1–27 (2021)

    Google Scholar 

  3. Zhao, M., Wei, G., Wei, C., Wu, J.: Pythagorean fuzzy todim method based on the cumulative prospect theory for magdm and its application on risk assessment of science and technology projects. Int. J. Fuzzy Syst. 2, 1–15 (2021)

    Google Scholar 

  4. Liao, N., Wei, G., Chen, X.: Todim method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting. Int. J. Fuzzy Syst. 7, 1–18 (2021)

    Google Scholar 

  5. Zhang, Z.M., Chen, S.: Group decision making based on acceptable multiplicative consistency and consensus of hesitant fuzzy linguistic preference relations. Inf. Sci. 541, 531–550 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  6. Zhong, X.Y., Xu, X.H.: Clustering-based method for large group decision making with hesitant fuzzy linguistic information: Integrating correlation and consensus. Appl. Soft Comput. 87, 105973 (2020)

    Article  Google Scholar 

  7. Xu, Y.J., Wen, X.W., Sun, H., Wang, H.M.: Consistency and consensus models with local adjustment strategy for hesitant fuzzy linguistic preference relations. Int. J. Fuzzy Syst. 20, 2216–2233 (2018)

    Article  MathSciNet  Google Scholar 

  8. Zhou, X.Y., Wang, L.Q., Liao, H.C., Wang, S.Y., Fujita, H.: A prospect theory-based group decision approach considering consensus for portfolio selection with hesitant fuzzy information. Knowl.-Based Syst. 168, 28–38 (2018)

    Article  Google Scholar 

  9. Liu, X., Xu, Y.J., Montes, R., Dong, Y.C., Herrera, F.: Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making. Int. J. Intell. Syst. 34, 920–946 (2019)

    Article  Google Scholar 

  10. Xu, Y.J., Cabrerizo, F., Herrera-Viedma, E.: A consensus model for hesitant fuzzy preference relations and its application in water allocation management. Appl. Soft Comput. 58, 265–284 (2017)

    Article  Google Scholar 

  11. Yager, R.R.: Quantifier guided aggregation using owa operators. Int. J. Intell. Syst. 11, 49–73 (1996)

    Article  Google Scholar 

  12. Zhang, B.W., Liang, H.M., Zhang, G.Q.: Reaching a consensus with minimum adjustment in magdm with hesitant fuzzy linguistic term sets. Information Fusion 42, 12–23 (2017)

    Article  Google Scholar 

  13. Liu, N.N., He, Y., Xu, Z.S.: A new approach to deal with consistency and consensus issues for hesitant fuzzy linguistic preference relations. Appl. Soft Comput. 76, 400–415 (2019)

    Article  Google Scholar 

  14. Xiao, J., Wang, X., Zhang, H.J.: Managing classification-based consensus in social network group decision making: An optimization-based approach with minimum information loss. Information Fusion 63, 74–87 (2020)

    Article  Google Scholar 

  15. Zhang, Z.M., Pedrycz, W.: A consistency and consensus-based goal programming method for group decision-making with interval-valued intuitionistic multiplicative preference relations. IEEE Transactions on Cybernetics 49, 3640–3654 (2019)

    Article  Google Scholar 

  16. Mohammadi, M., Rezaei, J.: Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega 96, 102254 (2020)

    Article  Google Scholar 

  17. Mohammadi, M., Rezaei, J.: Bayesian best-worst method: A probabilistic group decision making model. Omega 96, 102075 (2020)

    Article  Google Scholar 

  18. Wan, S.P., Zhong, L., Dong, J.Y.: A new method for group decision making with hesitant fuzzy preference relations based on multiplicative consistency. IEEE Trans. Fuzzy Syst. 99, 1–12 (2019)

    Article  Google Scholar 

  19. Wu, P., Liu, J.P., Zhou, L.G., Chen, H.Y.: Algorithm for improving additive consistency of linguistic preference relations with an integer optimization model. Appl. Soft Comput. 86, 105955 (2020)

    Article  Google Scholar 

  20. Cao, M.S., Wu, J., Chiclana, F., Urena, R., Herrera-Viedma, E.: A personalized consensus feedback mechanism based on maximum harmony degree. IEEE Transactions on Systems, Man, and Cybernetics: Systems 243, 713–721 (2019)

    Google Scholar 

  21. Pérez, I., Cabrerizo, F., Alonso, S., Dong, Y., Chiclana, F., Herrera-Viedma, E.: On dynamic consensus processes in group decision making problems. Inf. Sci. 459, 20–35 (2018)

    Article  Google Scholar 

  22. Tang, M., Zhou, X.Y., Liao, H.C., Xu, J.P., Fujita, H., Herrera, F.: Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowl.-Based Syst. 180, 62–74 (2019)

    Article  Google Scholar 

  23. Ben-Arieh, D., Easton, T.: Multi-criteria group consensus under linear cost opinion elasticity. Decis. Support Syst. 43, 713–721 (2007)

    Article  Google Scholar 

  24. Zhang, H.J., Dong, Y.C., Chiclana, F., Yu, S.: Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design. Eur. J. Oper. Res. 275, 580–598 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  25. Wu, Z.B., Huang, S., Xu, J.P.: Multi-stage optimization models for individual consistency and group consensus with preference relations. Eur. J. Oper. Res. 275, 182–194 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  26. Xiao, J., Wang, X., Zhang, H.J.: Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making. Information Fusion 53, 20–34 (2019)

    Article  Google Scholar 

  27. Zhang, B.W., Dong, Y.C., Zhang, H.J., Pedrycz, W.: Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory. Eur. J. Oper. Res. 287, 546–559 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  28. Liu, X., Xu, Y.J., Montes, R., Ding, R.X., Herrera, F.: Alternative ranking-based clustering and reliability index-based consensus reaching process for hesitant fuzzy large scale group decision making. IEEE Trans. Fuzzy Syst. 27, 159–171 (2019)

    Article  Google Scholar 

  29. Du, Z.J., Yu, S.M., Xu, X.H.: Managing noncooperative behaviors in large-scale group decision-making: Integration of independent and supervised consensus-reaching models. Inf. Sci. 531, 119–138 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  30. Gou, X.J., Xu, Z.S., Liao, H.C., Herrera, F.: Consensus model handling minority opinions and noncooperative behaviors in large-scale group decision-making under double hierarchy linguistic preference relations. IEEE Transactions on Cybernetics 99, 1–14 (2020)

    Google Scholar 

  31. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25, 529–539 (2010)

    MATH  Google Scholar 

  32. Xia, M.M., Xu, Z.S.: Hesitant fuzzy information aggregation in decision making. Int. J. Approximate Reasoning 52, 395–407 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhang, X.L., Xu, Z.S., Wang, H.: Heterogeneous multiple criteria group decision making with incomplete weight information: A deviation modeling approach. Information Fusion 25, 49–62 (2015)

    Article  Google Scholar 

  34. Chao, X.R., Kou, G., Peng, Y., Viedma, E.H.: Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. Eur. J. Oper. Res. 288, 271–293 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  35. Zhu, B., Xu, Z., Xu, J.: Deriving a ranking from hesitant fuzzy preference relations under group decision making. IEEE Transactions on Cybernetics 44, 1328–1337 (2013)

    Article  Google Scholar 

  36. Xu, Z.S., Xia, M.M.: Distance and similarity measures for hesitant fuzzy sets. Inf. Sci. 181, 2128–2138 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  37. Tversky, K.: Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  38. Wu, A.P., Li, H., Dong, M.: A novel two-stage method for matching the technology suppliers and demanders based on prospect theory and evidence theory under intuitionistic fuzzy environment. Appl. Soft Comput. 95, 106553 (2020)

    Article  Google Scholar 

  39. Wang, Z.L., Wang, Y.M.: Prospect theory-based group decision-making with stochastic uncertainty and 2-tuple aspirations under linguistic assessments. Information Fusion 56, 81–92 (2020)

    Article  Google Scholar 

  40. Li, J., Wang, J.Q., Hu, J.H.: Consensus building for hesitant fuzzy preference relations with multiplicative consistency. Comput. Ind. Eng. 128, 387–400 (2018)

    Article  Google Scholar 

  41. Kazmaier, J., Vuuren, J.: A generic framework for sentiment analysis: Leveraging opinion-bearing data to inform decision making. Decis. Support Syst. 135, 113304 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Grant (Nos.71971117) from NSF of China and the Grant (Nos.17YJA630035) from the Chinese Ministry of Education.

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Authors and Affiliations

  1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Xiwen Tao & Wenqi Jiang

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  1. Xiwen Tao
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  2. Wenqi Jiang
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Correspondence to Xiwen Tao.

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Tao, X., Jiang, W. Research on Two-Stage Hesitate Fuzzy Information Fusion Framework Incorporating Prospect Theory and Dichotomy Algorithm. Int. J. Fuzzy Syst. 24, 1530–1547 (2022). https://doi.org/10.1007/s40815-021-01207-6

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  • DOI: https://doi.org/10.1007/s40815-021-01207-6

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