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Measuring the outcome of movement-based three-way decision using proportional utility functions

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Abstract

The trisecting-acting-outcome (TAO) model of three-way decisions includes trisecting a universal set into three separate and closely connected regions, devising, and applying efficient strategies on the three regions, furthermore evaluating the outcome. This paper introduces the proportional utility function (PUF), representing the ratio between an object’s initial and final quantity, to measure the outcomes from two different perspectives for movement-based three-way decision. The first perspective, is that if each object produces the same benefits or costs when they have the same movement (call region-independent), we sum up the three regions’ utility as the overall outcome. The second scenario, is that each object generates a different cost or benefit, even if they have the same movements (call region-dependent). Here, finding an optimal investment plan is an essential matter based on their equivalent classification according to their specific characteristics. For a single equivalence class, we design a strategy to reach the goal under a specific investment, although this investment may be conservative. For all equivalence classes, we give one-time optimal investment plans using PUF based on invested or reserved resources in the movement-based three-way decision. Based on the program, we adopt a series of strategies in action under these investment budgets. The experimental results show that our strategy choices have practical significance and are in line with expected results.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the submitted article.

References

  1. Yao Y (2009) Three-way decision: an interpretation of rules in rough set theory. In: International Conference on Rough Sets and Knowledge Technology. Springer, pp 642–649

  2. Yao YY (2003) On generalizing rough set theory. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. Springer, pp 44–51

  3. Yao Y (2007) A note on definability and approximations. In: Transactions on rough sets VII. Springer, pp 274–282

  4. Yao Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341–353

    Article  MathSciNet  Google Scholar 

  5. Yao Y (2012) An outline of a theory of three-way decisions. In: International Conference on Rough Sets and Current Trends in Computing. Springer, pp 1–17

  6. Yao YY (2015) Rough set approximations: a concept analysis point of view. Comput Intell 1:282–296

    Google Scholar 

  7. Yao Y (2019) Tri-level thinking: models of three-way decision. Int J Mach Learn Cybern:1–13

  8. Wang P, Yao Y (2018) Ce3: a three-way clustering method based on mathematical morphology. Knowl-Based Syst 155:54–65

    Article  Google Scholar 

  9. Yu H, Chen L, Yao J, Wang X (2019) A three-way clustering method based on an improved dbscan algorithm. Physica A: Stat Mech Appl 535:122289

    Article  Google Scholar 

  10. Yue XD, Chen YF, Miao DQ, Fujita H (2020) Fuzzy neighborhood covering for three-way classification. Inf Sci 507:795–808

    Article  MathSciNet  MATH  Google Scholar 

  11. Wang P, Shi H, Yang X, Mi J (2019) Three-way k-means: integrating k-means and three-way decision.

  12. Azam N, Yao J (2014) Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets. Int J Approx Reason 55(1):142–155

    Article  MathSciNet  MATH  Google Scholar 

  13. Azam N, Yao J (2014) Game-theoretic rough sets for recommender systems. Knowl-Based Syst 72:96–107

    Article  Google Scholar 

  14. Yao J, Azam N (2014) Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans Fuzzy Syst 23(1):3–15

    Article  Google Scholar 

  15. Zhang H, Min F (2016) Three-way recommender systems based on random forests. Knowl-Based Syst 91:275–286

    Article  Google Scholar 

  16. Ren R, Wei L (2016) The attribute reductions of three-way concept lattices. Knowl-Based Syst 99:92–102

    Article  Google Scholar 

  17. Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl-Based Syst 91:143–151

    Article  Google Scholar 

  18. Qi J, Wei L, Yao Y (2014) Three-way formal concept analysis. In: International Conference on Rough Sets and Knowledge Technology. Springer, pp 732–741

  19. Yao Y, Qi J, Wei L (2018) Formal concept analysis, rough set analysis and granular computing based on three-way decisions. J Northwest Univ (Nat Sci Edn) 48(4):477–487

    MATH  Google Scholar 

  20. Yao Y (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20

    Article  Google Scholar 

  21. He X, Wei L, She Y (2018) L-fuzzy concept analysis for three-way decisions: basic definitions and fuzzy inference mechanisms. Int J Mach Learn Cybern 9(11):1857–1867

    Article  Google Scholar 

  22. Deng X, Yao Y (2014) Decision-theoretic three-way approximations of fuzzy sets. Inf Sci 279:702–715

    Article  MathSciNet  MATH  Google Scholar 

  23. She Y (2014) On determination of thresholds in three-way approximation of many-valued nm-logic. In: International Conference on Rough Sets and Current Trends in Computing. Springer, pp 136–143

  24. Hu M, Yao Y (2019) Structured approximations as a basis for three-way decisions in rough set theory. Knowl-Based Syst 165:92–109

    Article  Google Scholar 

  25. Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263

    Article  MATH  Google Scholar 

  26. Li J, Ren Y, Mei C, Qian Y, Yang X (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl-Based Syst 91:152–164

    Article  Google Scholar 

  27. Shivhare R, Cherukuri A K (2017) Three-way conceptual approach for cognitive memory functionalities. Int J Mach Learn Cybern 8(1):21–34

    Article  Google Scholar 

  28. Subramanian C M, Cherukuri A K, Chelliah C (2018) Role based access control design using three-way formal concept analysis. Int J Mach Learn Cybern 9(11):1807–1837

    Article  Google Scholar 

  29. Wang X, Li J (2018) Three-way decisions, concept lattice and granular computing. Springer

  30. Li J, Mei C, Xu W, Qian Y (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467

    Article  MathSciNet  MATH  Google Scholar 

  31. Jia X, Liao W, Tang Z, Shang L (2013) Minimum cost attribute reduction in decision-theoretic rough set models. Inf Sci 219:151–167

    Article  MathSciNet  MATH  Google Scholar 

  32. Wang G, Yu H, Li T, et al. (2014) Decision region distribution preservation reduction in decision-theoretic rough set model. Inf Sci 278:614–640

    Article  MathSciNet  MATH  Google Scholar 

  33. Yao Y, Fu R (2013) The concept of reducts in pawlak three-step rough set analysis. In: Transactions on Rough Sets XVI. Springer, pp 53–72

  34. Yao Y, Zhao Y, Wang J (2008) On reduct construction algorithms. In: Transactions on computational science II. Springer, pp 100–117

  35. Zhang X, Miao D (2014) Reduction target structure-based hierarchical attribute reduction for two-category decision-theoretic rough sets. Inf Sci 277:755–776

    Article  MathSciNet  MATH  Google Scholar 

  36. Jiang C, Wu J, Li Z (2019) Adaptive thresholds determination for saving cloud energy using three-way decisions. Clust Comput 22(4):8475–8482

    Article  Google Scholar 

  37. Jiang C, Duan Y, Yao J (2019) Resource-utilization-aware task scheduling in cloud platform using three-way clustering. J Intell Fuzzy Syst 37(4):5297–5305

    Article  Google Scholar 

  38. Ciucci D, Dubois D (2013) A map of dependencies among three-valued logics. Inf Sci 250:162–177

    Article  MathSciNet  MATH  Google Scholar 

  39. Hu B (2014) Three-way decisions space and three-way decisions. Inf Sci 281:21–52

    Article  MathSciNet  MATH  Google Scholar 

  40. Singh P K (2017) Three-way fuzzy concept lattice representation using neutrosophic set. Int J Mach Learn Cybern 8(1):69–79

    Article  Google Scholar 

  41. Singh P K (2018) Three-way n-valued neutrosophic concept lattice at different granulation. Int J Mach Learn Cybern 9(11):1839–1855

    Article  Google Scholar 

  42. Yao Y (2019) Three-way conflict analysis: Reformulations and extensions of the pawlak model. Knowl-Based Syst 180:26–37

    Article  Google Scholar 

  43. Sun L, Yin T, Ding W, Qian Y, Xu J (2021) Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy. IEEE Trans Fuzzy Syst

  44. Yao Y (2018) Three-way decision and granular computing. Int J Approx Reason 103:107–123

    Article  MATH  Google Scholar 

  45. Yao Y (2020) Three-way granular computing, rough sets, and formal concept analysis. Int J Approx Reason 116:106–125

    Article  MathSciNet  MATH  Google Scholar 

  46. Gao C, Yao Y (2017) Actionable strategies in three-way decisions. Knowl-Based Syst 133:141–155

    Article  Google Scholar 

  47. Jiang C, Yao Y (2018) Effectiveness measures in movement-based three-way decisions. Knowl-Based Syst 160:136–143

    Article  Google Scholar 

  48. Jiang C, Guo D, Duan Y, Liu Y (2020) Strategy selection under entropy measures in movement-based three-way decision. Int J Approx Reason 119:280–291

    Article  MathSciNet  MATH  Google Scholar 

  49. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  MATH  Google Scholar 

  50. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston

    Book  MATH  Google Scholar 

  51. Yao Y, Wang S, Deng X (2017) Constructing shadowed sets and three-way approximations of fuzzy sets. Inf Sci 412:132–153

    Article  MathSciNet  MATH  Google Scholar 

  52. Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B (Cybern) 28(1):103–109

    Article  Google Scholar 

  53. Hu M, Deng X, Yao Y (2019) An application of bayesian confirmation theory for three-way decision. Springer

  54. Hu M, Yao Y (2019) Structured approximations as a basis for three-way decisions in rough set theory. Knowl-Based Syst 165(4):92–109

    Article  Google Scholar 

  55. Zhao X, Hu B (2015) Fuzzy and interval-valued fuzzy decision-theoretic rough set approaches based on fuzzy probability measure. Inf Sci 298:534–554

    Article  MathSciNet  MATH  Google Scholar 

  56. Yang J, Yao Y (2020) Semantics of soft sets and three-way decision with soft sets. Knowl-Based Syst:105538. https://doi.org/10.1016/j.knosys.2020.105538

  57. Yang X, Li T, Fujita H, Liu D, Yao Y (2017) A unified model of sequential three-way decisions and multilevel incremental processing. Knowl-Based Syst 134:172–188

    Article  Google Scholar 

  58. Li H, Zhou X, Huang B, Liu D (2013) Cost-sensitive three-way decision: a sequential strategy. In: International Conference on Rough Sets and Knowledge Technology. Springer, pp 325–337

  59. Yang X, Li T, Liu D, Fujita H (2019) A temporal-spatial composite sequential approach of three-way granular computing. Inf Sci 486:171–189

    Article  Google Scholar 

  60. Yao Y (2016) Three-way decisions and cognitive computing. Cogn Comput 8(4):543–554

    Article  Google Scholar 

  61. Yao Y (2021) The geometry of three-way decision. Appl Intell:1–28

  62. Zhang X, Gou H, Lv Z, Miao D (2021) Double-quantitative distance measurement and classification learning based on the tri-level granular structure of neighborhood system. Knowl-Based Syst:106799

  63. Todhunter I (1865) A history of the mathematical theory of probability from the time of pascal to that of laplace. Macmillan

  64. Portugal RD, Svaiter B F (2011) Weber-fechner law and the optimality of the logarithmic scale. Mind Mach 21(1):73–81

    Article  Google Scholar 

  65. Dehaene S (2003) The neural basis of the weber–fechner law: a logarithmic mental number line. Trends Cogn Sci 7(4):145–147

    Article  MathSciNet  Google Scholar 

  66. Wang S (2017) New explanation on st.petersburg paradox-based on ratio utility theory. J Xi’an Jiaotong Univ (Soc Sci) 37(6):9–17

    Google Scholar 

  67. Sinn H (2003) Weber’s law and the biological evolution of risk preferences: The selective dominance of the logarithmic utility function, 2002 geneva risk lecture. Geneva Papers Risk Insur Theory 28(2):87–100

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by Natural Science Foundation of Heilongjiang Provincial (No.LH2020F031) and Postgraduate Innovation Project of Harbin Normal University (HSDSSCX2020-30).

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Correspondence to Doudou Guo.

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Jiang, C., Guo, D. & Xu, R. Measuring the outcome of movement-based three-way decision using proportional utility functions. Appl Intell 51, 8598–8612 (2021). https://doi.org/10.1007/s10489-021-02325-2

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