Skip to main content
SpringerLink
Log in

Measure Effectiveness of Change-based Three-way Decision Using Utility Theory

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The trisecting–acting–outcome (TAO) model of three-way decision plays a crucial role in solving complex problems. It comprises three components: trisecting, acting, and outcome. Trisecting means dividing an entire set into three relatively small parts based on an evaluation function, and acting means designing the corresponding action to apply to these three parts. The outcome denotes evaluation effectiveness based on the first two steps. Several previous investigations have measured effects from a coarse-grained perspective. Still, they have not considered the impact of fine-grained changes in objects and have not devised the method of selecting a strategy-constructed tripartition for a given expected outcome. The present study aimed to explore the outcome measure from the view of fine-grained changes in objects named change-based three-way decision. It also provides a novel approach to quantifying outcomes using utility theory. Specifically, we conducted the study using two perspectives: top-down and bottom-up, depending on the decision demands. The object is first divided into three regions based on changes corresponding to different utilities from the top-down view. Then, the overall utility is aggregated based on the particular utility metrics representing different satisfaction levels in the three regions. From the bottom-up perspective, we derive a pair of thresholds based on the desired utility of the decision-maker and thus obtain three parts whose changes can make by some action to achieve these effects. The principal conclusions of this exploration are that outcomes can be better ranked using objects’ changes using utility theory and that strategies can be better acting to achieve corresponding variations based on the expected utility of the decision-maker. We describe the specific steps and procedures of the proposed method through several examples. The findings presented in this paper add to our understanding of the outcome measure for the three-way decision.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

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

  2. Zhang Y, Miao D, Wang J, Zhang Z. A cost-sensitive three-way combination technique for ensemble learning in sentiment classification. Int J Approx Reason. 2019;105:85–97.

    Article  MathSciNet  Google Scholar 

  3. Zhang Y, Yao J. Gini objective functions for three-way classifications. Int J Approx Reason. 2017;81:103–14.

    Article  MathSciNet  Google Scholar 

  4. Yue X, Chen Y, Miao D, Fujita H. Fuzzy neighborhood covering for three-way classification. Inf Sci. 2020;507:795–808.

    Article  MathSciNet  Google Scholar 

  5. Zhang Q, Xia D, Wang G. Three-way decision model with two types of classification errors. Inf Sci. 2017;420:431–53.

    Article  MathSciNet  Google Scholar 

  6. Yu H, Wang X, Wang G, Zeng X. An active three-way clustering method via low-rank matrices for multi-view data. Inf Sci. 2020;507:823–39.

    Article  Google Scholar 

  7. Yu H, Chu S, Yang D. Autonomous knowledge-oriented clustering using decision-theoretic rough set theory. In: Proceeding of 5th International Conference on Rough Set and Knowledge Technology, vol. 6401. 2010. p. 687–694.

  8. Yu H, Zhang C, Wang G. A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl-Based Syst. 2016;91:189–203.

    Article  Google Scholar 

  9. Shah A, Azam N, Ali B, Khan MT, Yao J. A three-way clustering approach for novelty detection. Inf Sci. 2021;569:650–68.

    Article  MathSciNet  Google Scholar 

  10. Zhang H, Min F. Three-way recommender systems based on random forests. Knowl-Based Syst. 2016;91:275–86.

    Article  Google Scholar 

  11. Zhang H, Min F, Shi B. Regression-based three-way recommendation. Inf Sci. 2017;378:444–61.

    Article  Google Scholar 

  12. Qian F, Min Q, Zhao S, Chen J, Wang X, Zhang Y. Three-way decision collaborative recommendation algorithm based on user reputation. In: International Joint Conference on Rough Sets. Springer; 2019. p. 424–438.

  13. Xu Y, Zhang H, Min F, et al. Lecture notes in artificial intelligence. In: Polkowski L, Yao Y, Artiemjew P, Ciucci D, Liu D, Slezak D, et al., editors. A three-way recommender system for popularity-based costs, vol. 10314. Cham: Springer International Publishing Ag; 2017. p. 278–89.

    Google Scholar 

  14. Chen Y, Yue X, Fujita H, Fu S. Three-way decision support for diagnosis on focal liver lesions. Knowl-Based Syst. 2017;127:85–99.

    Article  Google Scholar 

  15. Zhi H, Qi J, Qian T, Wei L. Three-way dual concept analysis. Int J Approx Reason. 2019;114:151–65.

    Article  MathSciNet  Google Scholar 

  16. Yao Y. Three-way granular computing, rough sets, and formal concept analysis. Int J Approx Reason. 2020;116:106–25.

    Article  MathSciNet  Google Scholar 

  17. Qian T, Wei L, Qi J. A theoretical study on the object (property) oriented concept lattices based on three-way decisions. Soft Comput. 2019;23(19):9477–89.

    Article  Google Scholar 

  18. Fan B, Tsang E, Xu W, Chen D, Li W. Attribute-oriented cognitive concept learning strategy: a multi-level method. Int J Mach Learn Cybern. 2019;10(9):2421–37.

    Article  Google Scholar 

  19. Turney P. Types of cost in inductive concept learning. arXiv preprint cs/0212034. 2002.

  20. Ganter B, Wille R. Formal concept analysis: mathematical foundations. Springer Science & Business Media; 2012.

  21. Yao Y. Three-way conflict analysis: Reformulations and extensions of the Pawlak model. Knowl-Based Syst. 2019;180:26–37.

    Article  Google Scholar 

  22. Sun B, Chen X, Zhang L, Ma W. 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  Google Scholar 

  23. Yao Y. Probabilistic rough set approximations. Int J Approx Reason. 2008;49(2):255–71.

    Article  Google Scholar 

  24. Yao Y. Information granulation and approximation in a decision-theoretical model of rough sets. In: Polkowski SKSAE L Pal, editor. Rough-neuro computing: techniques for computing with words. Springer, Berlin; 2003. p. 491–516.

  25. Stepaniuk J. Approximation spaces, reducts and representatives. In: Rough sets in knowledge discovery 2. Springer; 1998. p. 109–126.

  26. Qian Y, Liang J, Pedrycz W, Dang C. positive approximation: An accelerator for attribute reduction in rough set theory. Artif Intell. 2010;174(9):597–618.

    Article  MathSciNet  Google Scholar 

  27. Li W, Jia X, Wang L, Zhou B. Multi-objective attribute reduction in three-way decision-theoretic rough set model. Int J Approx Reason. 2019;105:327–41.

    Article  MathSciNet  Google Scholar 

  28. Li M, Wang G. Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts. Knowl-Based Syst. 2016;91:165–78.

    Article  Google Scholar 

  29. Ma X, Yao Y. Three-way decision perspectives on class-specific attribute reducts. Inf Sci. 2018;450:227–45.

    Article  MathSciNet  Google Scholar 

  30. Ma X, Wang G, Yu H, Li T. Decision region distribution preservation reduction in decision-theoretic rough set model. Inf Sci. 2014;278:614–40.

    Article  MathSciNet  Google Scholar 

  31. Zhou B, Yao Y, Luo J. A three-way decision approach to email spam filtering. In: Proceedings of the 23th Canadian Conference on Artificial Intelligence. vol. 6085; 2010. p. 514–522.

  32. Jiang C, Wu J, Li Z. Adaptive thresholds determination for saving cloud energy using three-way decisions. Clust Comput. 2019;22(4):8475–82.

    Article  Google Scholar 

  33. Jiang C, Duan Y, Yao J. Resource-utilization-aware task scheduling in cloud platform using three-way clustering. J Intell Fuzzy Syst. 2019;37(4):5297–305.

    Article  Google Scholar 

  34. Jia X, Shang L. Three-way decisions versus two-way decisions on filtering spam email. In: Transactions on Rough Sets XVIII. Springer; 2014. p. 69–91.

  35. Hu J, Yang Y, Chen X. A novel TODIM method-based three-way decision model for medical treatment selection. Int J Fuzzy Syst. 2018;20(4):1240–55.

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  37. Jia X, Deng Z, Min F, Liu D. Three-way decisions based feature fusion for Chinese irony detection. Int J Approx Reason. 2019;113:324–35.

    Article  Google Scholar 

  38. Yao Y. Three-way decisions and cognitive computing. Cogn Comput. 2016;8(4):543–54.

    Article  Google Scholar 

  39. Yao Y. Three-way decision and granular computing. Int J Approx Reason. 2018;103:107–23.

    Article  Google Scholar 

  40. Jiang C, Yao Y. Effectiveness measures in movement-based three-way decisions. Knowl-Based Syst. 2018;160:136–43.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Natural Science Foundation of Heilongjiang Province ( LH2020F031).

Funding

This study was funded by the Natural Science Foundation of Heilongjiang Province ( LH2020F031).

Author information

Authors and Affiliations

  1. College of Computer Science and Information Engineer, Harbin Normal University, Harbin, Heilongjiang Province, 150025, China

    Chunmao Jiang

  2. College of Artificial Intelligence, Southwest University, 400715, Chongqing, P.R. China

    Doudou Guo

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Ying Duan

Authors
  1. Chunmao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Doudou Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunmao Jiang.

Ethics declarations

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Research Involving Human and Animal Participants

This article does not contain any studies with human participants or animals performed by any authors.

Conflicts of Interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, C., Guo, D. & Duan, Y. Measure Effectiveness of Change-based Three-way Decision Using Utility Theory. Cogn Comput 14, 1009–1018 (2022). https://doi.org/10.1007/s12559-022-09999-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-022-09999-x

Keywords

Search

Navigation