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Three-way Conflict Analysis: Alliance, Conflict, and Neutrality Reducts of Three-valued Situation Tables

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Abstract

In a three-valued situation table, there are different levels of importance for different issues with respect to the alliance, conflict, and neutrality relations, and it is necessary to distinguish all issues as reducible and irreducible elements to filter out the key issues for solving conflicts. However, we have not observed studies of issue reduction for three-valued situation tables in three-way conflict analysis. In this paper, first, we give the matrix representations of alliance, conflict, and neutrality relations and reveal the relationship between two agents in the framework of a matrix. Then, we propose the concepts of alliance, conflict, and neutrality reducts of three-valued situation tables and define reducible and irreducible elements with respect to the alliance, conflict, and neutrality relations. Additionally, we design sequential forward and backward heuristic algorithms for constructing the alliance, conflict, and neutrality reducts. Finally, we give discernibility matrices for computing the sets of all alliance, conflict, and neutrality reducts, and we employ several examples to illustrate how to construct all alliance, conflict, and neutrality reducts. We provide an application of issue reduction to help the government of Gansu Province make a development plan for next year.

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References

  1. Pawlak Z. About conflicts, ICS PAS Reports. 1981:451.

  2. Pawlak Z. On conflicts. Int J Man Mach Stud. 1984;21:127–34.

    Article  MATH  Google Scholar 

  3. Pawlak Z. An inquiry into anatomy of conflicts. Inf Sci. 1998;109:65–78.

    Article  MathSciNet  Google Scholar 

  4. Deja R. Conflict analysis. Int J Intell Syst. 2002;17:235–53.

    Article  MATH  Google Scholar 

  5. Deja R. Conflict analysis, rough set methods and applications. Stud Fuzz Soft Computing. 2000:491–520.

  6. Pawlak Z. Some remarks on conflict analysis. Eur J Oper Res. 2005;166:649–54.

    Article  MATH  Google Scholar 

  7. Ramanna S, Peters J, Skowron A. Approaches to conflict dynamics based on rough sets. Fund Inform. 2007;75:453–68.

    MathSciNet  MATH  Google Scholar 

  8. Skowron A, Deja R. On some conflict models and conflict resolutions, Romanian. J Inf Sci Technol. 2002;5:69–82.

    Google Scholar 

  9. Yu T, Liang X, Shen GQ, Shi Q, Wang G. An optimization model for managing stakeholder conflicts in urban redevelopment projects in China. J Clean Prod. 2019;212:537–47.

    Article  Google Scholar 

  10. Yao YY. Three-way decisions with probabilistic rough sets. Inf Sci. 2010;180:341–53.

    Article  MathSciNet  Google Scholar 

  11. Yao YY. Tri-level thinking: models of three-way decision. Int J Mach Learn Cybern. 2020;11:947–59.

    Article  Google Scholar 

  12. Yao YY, Wong SKM, Wang LS. A non-numeric approach to uncertain reasoning. Int J Gen Syst. 1995;23:343–59.

    Article  MATH  Google Scholar 

  13. Qi JJ, Qian T, Wei L. The connections between three-way and classical concept lattices. Knowl-Based Syst. 2016;91:143–51.

    Article  Google Scholar 

  14. Yao YY. Three-way decision and granular computing. Int J Approximate Reasoning. 2018;103:107–23.

    Article  MATH  Google Scholar 

  15. Zhang YJ, Miao DQ, Zhang ZF, Xu JF, Luo S. A three-way selective ensemble model for multi-label classification. Int J Approximate Reasoning. 2018;103:394–413.

    Article  MathSciNet  MATH  Google Scholar 

  16. Azam N, Zhang Y, Yao JT. Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets. Eur J Oper Res. 2017;261(2):704–14.

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhang Y, Yao JT. Game theoretic approach to shadowed sets: A three-way tradeoff perspective. Inf Sci. 2020;507:540–52.

    Article  MATH  Google Scholar 

  18. Fan Y, Qi JJ, Wei L. A conflict analysis model based on three-way decisions. Lecture Notes Artificial Intelligence. 2018;11103:522–32.

    Google Scholar 

  19. Lang GM, Luo JF, Yao YY. Three-way conflict analysis: A unification of models based on rough sets and formal concept analysis. Knowl-Based Syst. 2020. https://doi.org/10.1016/j.knosys.2020.105556.

    Article  Google Scholar 

  20. Li XN, Wang X, Lang GM, Yi HJ. Conflict analysis based on three-way decision for triangular fuzzy information systems. Int J Approximate Reasoning. 2021;132:88–106.

    Article  MathSciNet  MATH  Google Scholar 

  21. Sun BZ, Ma WM, Zhao HY. Rough set-based conflict analysis model and method over two universes. Inf Sci. 2016;372:111–25.

    Article  MATH  Google Scholar 

  22. Yao YY. Three-way conflict analysis: reformulations and extensions of the Pawlak model. Knowl-Based Syst. 2019;180:26–37.

    Article  Google Scholar 

  23. Lang GM, Miao DQ, Fujita H. Three-way group conflict analysis based on Pythagorean fuzzy set theory. IEEE Trans Fuzzy Syst. 2020;28(3):447–61.

    Article  Google Scholar 

  24. Lang GM, Yao YY. New measures of alliance and conflict for three-way conflict analysis. Int J Approximate Reasoning. 2021;132:49–69.

    Article  MathSciNet  MATH  Google Scholar 

  25. Feng QR, Zhou Y. Soft discernibility matrix and its applications in decision making. Appl Soft Comput. 2014;24:749–56.

    Article  Google Scholar 

  26. Ma FM, Ding MW, Zhang TF, Cao J. Compressed binary discernibility matrix based incremental attribute reduction algorithm for group dynamic data. Neurocomputing. 2019;344:20–7.

    Article  Google Scholar 

  27. Qian WB, Xiong CZ, Wang YL. A ranking-based feature selection for multi-label classification with fuzzy relative discernibility. Appl Soft Comput. 2021;102.

    Article  Google Scholar 

  28. Tsang ECC, Chen DG, Yeung DS. Approximations and reducts with covering generalized rough sets. Comput Math Appl. 2008;56(1):279–89.

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang JD, Qian YH, Li FJ, Liang JY, Ding WP. Fusing Fuzzy Monotonic Decision Trees. IEEE Trans Fuzzy Syst. 2020;28(5):887–99.

    Article  Google Scholar 

  30. Wang CZ, Shao MW, Sun BQ, Hu QH. An improved attribute reduction scheme with covering based rough sets. Appl Soft Comput. 2015;26:235–43.

    Article  Google Scholar 

  31. Wei W, Wu XY, Liang JY, Cui JB, Sun YJ. Discernibility matrix based incremental attribute reduction for dynamic data. Knowl-Based Syst. 2018;140:142–57.

    Article  Google Scholar 

  32. Yao YY, Zhao Y. Discernibility matrix simplification for constructing attribute reducts. Inf Sci. 2009;179:867–82.

    Article  MathSciNet  MATH  Google Scholar 

  33. Yuan Z, Chen HM, Xie P, Zhang PF, Liu J, Li TR. Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions. Appl Soft Comput. 2021;107.

    Article  Google Scholar 

  34. Sun BZ, Chen XT, Zhang LY, Ma WM. Three-way decision making approach to conflict analysis and resolution using probabilistic rough set over two universes. Inf Sci. 2020;507:809–22.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

We would like to thank the anonymous reviewers very much for their professional comments and valuable suggestions. This work was supported in part by the National Natural Science Foundation of China (Nos. 62076040, 61603063), Hunan Provincial Natural Science Foundation of China (Nos. 2020JJ3034, 2020JJ4598), the Scientific Research Fund of Chongqing Key Laboratory of Computational Intelligence (No. 2020FF04), the Scientific Research Fund of Hunan Provincial Education Department (Nos. 18C0220, 19B027), and a Discovery Grant from the NSERC, Canada.

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Correspondence to Guangming Lang.

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Lang, G. Three-way Conflict Analysis: Alliance, Conflict, and Neutrality Reducts of Three-valued Situation Tables. Cogn Comput 14, 2040–2053 (2022). https://doi.org/10.1007/s12559-021-09905-x

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