Skip to main content
Log in

PROMETHEE II method based on variable precision fuzzy rough sets with fuzzy neighborhoods

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The model of covering-based fuzzy rough sets (CFRSs) can be regarded as a hybrid one by combining covering-based rough sets with fuzzy sets. In this paper, based on fuzzy neighborhoods, we propose two types of covering-based variable precision fuzzy rough sets (CVPFRSs) via fuzzy logical operators, i.e., type-I CVPFRSs and type-II CVPFRSs. Then, several basic properties of the two types of CVPFRSs are discussed. In addition, by virtue of the idea of PROMETHEE II methods, we construct a novel method to multi-attribute decision-making (MADM) in the context of medical diagnosis based on the proposed rough approximation operators. Finally, a test example for illustrating the proposed method is given. Meanwhile, a comparative analysis and an experimental evaluation are further discussed to interpret and evaluate the effectiveness and superiority of the proposed method. The proposed rough set model not only extends the theory of CFRSs, but also provides a new perspective for MADM with fuzzy evaluation information.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

References

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    Article  MATH  Google Scholar 

  • Brans JP, Vincke P, Mareschal B (1986) How to select and how to rank projects: the PROMETHEE method. Eur J Oper Res 24:228–238

    Article  MathSciNet  MATH  Google Scholar 

  • De Baets B (1997) Coimplicators, the forgotten connectives. Tatra Mt Math Publ 12:229–240

    MathSciNet  MATH  Google Scholar 

  • D’eer L, Cornelis C (2018) A comprehensive study of fuzzy covering-based rough set models: definitions, properties and interrelationships. Fuzzy Sets Syst 336:1–26

    Article  MathSciNet  MATH  Google Scholar 

  • D’eer L, Cornelis C, Godo L (2017) Fuzzy neighborhood operators based on fuzzy covering. Fuzzy Sets Syst 312:17–35

    Article  MathSciNet  MATH  Google Scholar 

  • Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–209

    Article  MATH  Google Scholar 

  • Feng T, Mi JS (2016) Variable precision multigranulation decision-theoretic fuzzy rough sets. Knowl-Based Syst 91:93–101

    Article  Google Scholar 

  • Ghorabaee MK, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451

    Article  Google Scholar 

  • Ghorabaee MK, Zavadskas EK, Amiri M, Turskis Z (2016) Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. Int J Comput Commun Control 11(3):358–371

    Article  Google Scholar 

  • Goumas M, Lygerou V (2000) An extension of the PROMETHEE method for decision making in fuzzy environment: ranking of alternative energy exploitation projects. Eur J Oper Res 123:606–613

    Article  MATH  Google Scholar 

  • Han SE (2019) Roughness measures of locally finite covering rough sets. Int J Approx Reason 105:368–385

    Article  MathSciNet  MATH  Google Scholar 

  • Harsanyi JC (1955) Cardinal welfare, individualistic ethics and interpersonal comparisons of utility. J Polit Econ 63:309–321

    Article  Google Scholar 

  • Huang B, Guo CX, Li HX, Feng GF, Zhou XZ (2016) An intuitionistic fuzzy graded covering rough set. Knowl-Based Syst 107:155–178

    Article  Google Scholar 

  • Huang B, Li HX, Feng GF, Guo CX (2020) Intuitionistic fuzzy \(\beta \)-covering-based rough sets. Artif Intell Rev 53:2841–2873

    Article  Google Scholar 

  • Hwang CL, Yoon K (1981) Multiple attributes decision making methods and applications. Springer, Berlin

    Book  MATH  Google Scholar 

  • Jia F, Liu PD (2019) A novel three-way decision model under multiple-criteria environment. Inf Sci 471:29–51

    Article  MathSciNet  MATH  Google Scholar 

  • Jiang HB, Zhan JM, Chen DG (2019) Covering based variable precision (\({\cal{I}}\), \({\cal{T}}\))-fuzzy rough sets with applications to multi-attribute decision-making. IEEE Trans Fuzzy Syst 27(8):1558–1572

    Article  Google Scholar 

  • Kacprzak D (2020) An extended TOPSIS method based on ordered fuzzy numbers for group decision making. Artif Intell Rev 53:2099–2129

    Article  Google Scholar 

  • Li TJ, Leung Y, Zhang WX (2008) Generalized fuzzy rough approximation operators based on fuzzy covering. Int J Approx Reason 48:836–856

    Article  MathSciNet  MATH  Google Scholar 

  • Liang DC, Liu D (2014) Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets. Inf Sci 276:186–203

    Article  Google Scholar 

  • Liang DC, Xu ZS (2017) The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Appl Soft Comput 60:167–179

    Article  Google Scholar 

  • Liu D, Liang DC (2017) Three-way decisions in ordered decision system. Knowl-Based Syst 137:182–195

    Article  Google Scholar 

  • Ma LW (2012) On some types of neighborhood-related covering rough set. Int J Approx Reason 53:901–911

    Article  MathSciNet  MATH  Google Scholar 

  • Ma LW (2016) Two fuzzy covering rough set models and their generalizations over fuzzy lattices. Fuzzy Sets Syst 294:1–17

    Article  MathSciNet  MATH  Google Scholar 

  • Mardani A, Jusoh A, Zavadskas EK (2015) Fuzzy multiple criteria decision-making techniques and applications—two decades review from 1994 to 2014. Expert Syst Appl 42:4126–4148

    Article  Google Scholar 

  • Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156:445–455

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Peng XD, Selvachandran G (2019) Pythagorean fuzzy set: state of the art and future directions. Artif Intell Rev 52:1873–1927

    Article  Google Scholar 

  • Pomykala JA (1987) Approximation operations in approximation spaces. Bull Pol Acad Sci Math 35:653–662

    MathSciNet  MATH  Google Scholar 

  • Radzikowska AM, Kerre EE (2002) A comparative study of fuzzy rough sets. Fuzzy Sets Syst 126:137–155

    Article  MathSciNet  MATH  Google Scholar 

  • Song JJ, Tsang ECC, Chen DG, Yang XB (2017) Minimal decision cost reduct in fuzzy decision-theoretic rough set model. Knowl-Based Syst 126(115):104–114

    Article  Google Scholar 

  • Sun BZ, Ma WM, Chen XT, Li XN (2018a) Heterogeneous multigranulation fuzzy rough set-based multiple attribute group decision making with heterogeneous preference information. Comput Ind Eng 122:24–38

    Article  Google Scholar 

  • Sun BZ, Ma WM, Li BJ, Li XN (2018b) Three-way decisions approach to multiple attribute group decision making with linguistic information-based decision-theoretic rough fuzzy set. Int J Approx Reason 93:424–442

    Article  MathSciNet  MATH  Google Scholar 

  • Wang CY, Hu BQ (2015) Granular variable precision fuzzy rough sets with general fuzzy relations. Fuzzy Sets Syst 275:39–57

    Article  MathSciNet  MATH  Google Scholar 

  • Wang CZ, Huang Y, Shao MW, Fan XD (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl-Based Syst 164:205–212

    Article  Google Scholar 

  • Wu P, Wu Q, Zhou LG, Chen HY, Zhou H (2019a) A consensus model for group decision making under trapezoidal fuzzy numbers environment. Neural Comput Appl 31:377–394

    Article  Google Scholar 

  • Wu P, Zhou LG, Chen HY, Tao ZF (2019b) Additive consistency of hesitant fuzzy linguistic preference relation with a new expansion principle for hesitant fuzzy linguistic term sets. IEEE Trans Fuzzy Syst 27(4):716–730

    Article  Google Scholar 

  • Wu P, Zhu JM, Zhou LG, Chen HY (2019c) Local feedback mechanism based on consistency-derived for consensus building in group decision making with hesitant fuzzy linguistic preference relations. Comput Ind Eng 137:106001

    Article  Google Scholar 

  • Wu P, Liu JP, Zhou LG, Chen HY (2020a) Algorithm for improving additive consistency of linguistic preference relations with an integer optimization model. Appl Soft Comput 86:105955

    Article  Google Scholar 

  • Wu P, Zhou LG, Chen HY, Tao ZF (2020b) Multi-stage optimization model for hesitant qualitative decision making with hesitant fuzzy linguistic preference relations. Appl Intell 50:222–240

    Article  Google Scholar 

  • Xu ZS, Zhao N (2016) Information fusion for intuitionistic fuzzy decision making: an overview. Inf Fusion 28:10–23

    Article  Google Scholar 

  • Yager RR (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18:183–190

    Article  MATH  Google Scholar 

  • Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22:958–965

    Article  Google Scholar 

  • Yang B, Hu BQ (2018) Communication between fuzzy information systems using fuzzy covering-based rough sets. Int J Approx Reason 103:414–436

    Article  MathSciNet  MATH  Google Scholar 

  • Yao YY (2010) Three-way decision with probabilistic rough set models. Inf Sci 180(3):341–353

    Article  Google Scholar 

  • Yao YY (2011) The superiority of three-way decisions in probabilistic rough set models. Inf Sci 181:1080–1096

    Article  MathSciNet  MATH  Google Scholar 

  • Yao YQ, Mi JS, Li ZJ (2014) A novel variable (\(\theta \), \(\sigma \))-fuzzy rough set model based on fuzzy granules. Fuzzy Sets Syst 236:58–72

    Article  MathSciNet  MATH  Google Scholar 

  • Yeung DS, Chen DG, Tsang ECC, Lee JWT, Wang XZ (2005) On the generalization of fuzzy rough sets. IEEE Trans Fuzzy Syst 13:343–361

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  • Zhan JM, Xu WH (2020) Two types of coverings based multigranulation rough fuzzy sets and applications to decision making. Artif Intell Rev 53:167–198

    Article  Google Scholar 

  • Zhan JM, Sun B, Alcantud JCR (2019) Covering based multigranulation \((I, T)\)-fuzzy rough set models and applications in multi-attribute group decision-making. Inf Sci 476:290–318

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang XL, Xu ZS (2014) Extension of TOPSIS to multiple criteria decision making with pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078

    Article  Google Scholar 

  • Zhang C, Li DY, Ren R (2016) Pythagorean fuzzy multigranulation rough set over two universes and its applications in merger and acquisition. Int J Intell Syst 31(9):921–943

    Article  Google Scholar 

  • Zhao XR, Hu BQ (2015) Fuzzy variable precision rough sets based on residuated lattices. Int J Gen Syst 44:743–765

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao SY, Tsang ECC, Chen DG (2009) The model of fuzzy variable precision rough sets. IEEE Trans Fuzzy Syst 17:451–467

    Article  Google Scholar 

  • Zhu W, Wang FY (2007) On three types of covering rough sets. IEEE Trans Knowl Data Eng 19:1131–1144

    Article  Google Scholar 

  • Ziarko W (1993) Variable precision rough set model. J Comput Syst Sci 46:39–59

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors are extremely grateful to the editors and two reviewers for their valuable comments and helpful suggestions which helped to improve the presentation of this paper. This research is partially supported by NNSFC (11961025; 61866011; 11461025; 11561023).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianming Zhan.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

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, H., Zhan, J. & Chen, D. PROMETHEE II method based on variable precision fuzzy rough sets with fuzzy neighborhoods. Artif Intell Rev 54, 1281–1319 (2021). https://doi.org/10.1007/s10462-020-09878-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-020-09878-7

Keywords

Navigation