Skip to main content
Log in

The multi-fuzzy N-soft set and its applications to decision-making

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The goal of this paper is to introduce a novel hybrid model called multi-fuzzy N-soft set, and to design an adjustable decision-making methodology for solving problems where the inputs appear in this form. The new model enhances the virtues of multi-fuzzy set theory with the benefits of N-soft sets, two models that have been extensively investigated in recent years. The theoretical setting that arises allows us to incorporate data on the occurrence of ratings or grades (the defining characteristic of N-soft sets) in a multi-fuzzy environment. We perform a set-theoretical analysis of multi-fuzzy N-soft sets in order to establish the fundamental properties of their behavior. Then we develop a highly adaptable approach to decision-making in this new setting. This methodology takes advantage of a flexible procedure for the conversion of the original data to a hesitant N-soft setting, where we can resort to scores. Examples illustrate its application and the role of each parameter in the decision-making procedure.

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.

Similar content being viewed by others

References

  1. Azam M, Bouguila N (2019) Bounded generalized Gaussian mixture model with ICA. Neural Process Lett 49:1299–1320

    Google Scholar 

  2. Azam M, Bouguila N (2020) Multivariate bounded support Laplace mixture model. Soft Comput 24:13239–13268

    Google Scholar 

  3. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  4. Molodtsov D (1999) Soft set theory-first results. Comput Math Appl 37(4–5):19–31

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  6. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    MATH  Google Scholar 

  7. Liu X, Kim H, Feng F, Alcantud JCR (2018) Centroid transformations of intuitionistic fuzzy values based on aggregation operators. Mathematics 6(11):215

    MATH  Google Scholar 

  8. Akram M, Ali G, Alcantud JCR (2019) New decision-making hybrid model: intuitionistic fuzzy \(N\)-soft rough sets. Soft Comput 23(20):9853–9868

    MATH  Google Scholar 

  9. Wang JQ, Han ZQ, Zhang HY (2014) Multi-criteria group decision-making method based on intuitionistic interval fuzzy information. Group Decis Negot 23:715–733

    Google Scholar 

  10. Abdulkareem KH, Arbaiy N, Zaidan AA et al (2020) A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05020-4

    Article  Google Scholar 

  11. Abdulkareem KH, Arbaiy N, Zaidan AA et al (2020) A novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques. Int J Inf Technol Decis Making 19(3):909–957

    Google Scholar 

  12. Mohammed MA, Abdulkareem KH et al (2020) Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. IEEE Access 8:99115–99131

    Google Scholar 

  13. Mizumoto M, Tanaka K (1976) Some properties of fuzzy sets of type-2. Inf Control 31:312–340

    MathSciNet  MATH  Google Scholar 

  14. Xia MM, Xu ZS (2011) Hesitant fuzzy information aggregation in decision-making. Int J Appr Reason 52:395–407

    MathSciNet  MATH  Google Scholar 

  15. Zhu B, Xu ZS, Xu JP (2014) Deriving a ranking from hesitant fuzzy preference relations under group decision-making. IEEE Trans Cybern 44(8):1328–119

    Google Scholar 

  16. Fatimah F, Alcantud JCR (2018) Expanded dual hesitant fuzzy sets, In: 2018 International Conference on Intelligent Systems (IS), https://doi.org/10.1109/IS.2018.8710539, pp 102–108

  17. Liu P, Zhang L (2017) Multiple criteria decision-making method based on neutrosophic hesitant fuzzy Heronian mean aggregation operators. J Intell Fuzzy Syst 32(1):303–319

    MATH  Google Scholar 

  18. Liu P, Zhang L (2017) An extended multiple criteria decision-making method based on neutrosophic hesitant fuzzy information. J Intell Fuzzy Syst 32(6):4403–4413

    MATH  Google Scholar 

  19. Peng X, Dai J (2017) Hesitant fuzzy soft decision-making methods based on WASPAS, MABAC and COPRAS with combined weights. J Intell Fuzzy Syst 33(2):1313–1325

    MATH  Google Scholar 

  20. Peng X, Yang Y (2015) Approaches to interval-valued intuitionistic hesitant fuzzy soft sets based decision-making. Ann Fuzzy Math Inf 10(4):657–680

    MathSciNet  MATH  Google Scholar 

  21. Peng X, Yang Y (2015) Interval-valued hesitant fuzzy soft sets and their application in decision-making. Fundamenta Informaticae 141(1):71–93

    MathSciNet  MATH  Google Scholar 

  22. Fatimah F, Rosadi D, Hakim RBF, Alcantud JCR (2018) \(N\)-soft sets and their decision-making algorithms. Soft Comput 22(12):3829–3842

    MATH  Google Scholar 

  23. Maji PK, Biswas R, Roy AR (2003) Soft set theory. Comput Math Appl 45(4–5):555–562

    MathSciNet  MATH  Google Scholar 

  24. Ali MI, Feng F, Liu XY, Min WK, Shabir M (2009) On some new operations in soft set theory. Comput Math Appl 57(9):1547–1553

    MathSciNet  MATH  Google Scholar 

  25. Maji PK, Biswas R, Roy AR (2002) An application of soft sets in decision-making problem. Comput Math Appl 44(8–9):1077–1083

    MathSciNet  MATH  Google Scholar 

  26. Maji PK, Biswas R, Roy AR (2001) Fuzzy soft sets. J Fuzzy Math 9(3):589–602

    MathSciNet  MATH  Google Scholar 

  27. Alcantud JCR, Mathew TJ (2017) Separable fuzzy soft sets and decision making with positive and negative attributes. Appl Soft Comput 59:586–595

    Google Scholar 

  28. Alcantud JCR, Cruz-Rambaud S, Torrecillas MJ Muñoz (2017) Valuation fuzzy soft sets: a flexible fuzzy soft set based decision making procedure for the valuation of assets. Symmetry 9:253

    MATH  Google Scholar 

  29. Majumdar P, Samanta SK (2010) Generalized fuzzy soft sets. Comput Math Appl 59(4):1425–1432

    MathSciNet  MATH  Google Scholar 

  30. Alcantud JCR, Torrecillas MJ Muñoz (2017) Intertemporal choice of fuzzy soft sets. Symmetry 9:253

    Google Scholar 

  31. Peng XD, Liu C (2017) Algorithms for neutrosophic soft decision-making based on EDAS, new similarity measure and level soft set. J Intell Fuzzy Syst 32(1):955–968

    MATH  Google Scholar 

  32. Peng XD, Yang Y (2017) Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision-making based on regret theory and prospect theory with combined weight. Appl Soft Comput 54:415–430

    Google Scholar 

  33. Peng XD, Garg H (2018) Algorithms for interval-valued fuzzy soft sets in emergency decision-making based on WDBA and CODAS with new information measure. Comput Ind Eng 119:439–452

    Google Scholar 

  34. Zhan J, Alcantud JCR (2019) A survey of parameter reduction of soft sets and corresponding algorithms. Artif Intell Rev 52(3):1839–1872

    Google Scholar 

  35. Ma X, Liu Q, Zhang J (2017) A survey of decision-making methods based on certain hybrid soft set models. Artif Intell Rev 47(4):507–530

    Google Scholar 

  36. Alcantud JCR, Santos-García G (2017) A new criterion for soft set based decision-making problems under incomplete information. Int J Comput Intell Syst 10:394–404

    Google Scholar 

  37. Herawan T, Deris MM (2009) On multi-soft sets construction in information systems. In: International Conference on Intelligent Computing, Springer, Berlin, pp 101–110

  38. Chen S, Liu J, Wang H, Augusto JC (2013) Ordering based decision-making a survey. Inf Fus 14(4):521–531

    Google Scholar 

  39. Ali MI, Mahmood T, Rehman MMU, Aslam MF (2015) On lattice ordered soft sets. Appl Soft Comput 36:499–505

    Google Scholar 

  40. Fatimah F, Rosadi D, Hakim RBF, Alcantud JCR (2019) Probabilistic soft sets and dual probabilistic soft sets in decision-making. Neural Comput Appl 31(Suppl 1:397):397–407

    Google Scholar 

  41. Fatimah F, Rosadi D, Hakim RBF (2018) Probabilistic soft sets and dual probabilistic soft sets in decision making with positive and negative parameters. J Phys Conf Ser 983(1):012112

    Google Scholar 

  42. Fatimah F, Rosadi D, Hakim RBF, Alcantud JCR (2017) A social choice approach to graded soft sets, in: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, 1-6

  43. Akram M, Adeel A, Alcantud JCR (2018) Fuzzy \(N\)-soft sets: a novel model with applications. J Intell Fuzzy Syst 35(4):4757–4771

    Google Scholar 

  44. Alcantud JCR, Feng F, Yager RR (2020) An \(N\)-soft set approach to rough sets. IEEE Trans Fuzzy Syst 28(11):2996–3007

    Google Scholar 

  45. Kamacı H, Petchimuthu S (2020) Bipolar \(N\)-soft set theory with applications. Soft Comput 24:16727–16743

    Google Scholar 

  46. Chen Y, Liu J, Chen Z, Zhang Y (2020) Group decision-making method based on generalized vague \(N\)-soft sets, In: 2020 Chinese Control And Decision Conference (CCDC), pp 4010–4015

  47. Riaz M, Naeem K, Zareef I, Afzal D (2020) Neutrosophic \(N\)-soft sets with TOPSIS method for multiple attribute decision making. Neutrosophic Sets an Syst 32:1–23

    Google Scholar 

  48. Liu J, Chen Y, Chen Z, Zhang Y (2020) Multi-attribute decision making method based on neutrosophic vague \(N\)-soft sets. Symmetry 12:853

    Google Scholar 

  49. Akram M, Ali G, Alcantud JCR, Fatimah F (2020) Parameter reductions in \(N\)-soft sets and their applications in decision-making, Expert Systems, in Press

  50. Akram M, Adeel A, Alcantud JCR (2019) Group decision-making methods based on hesitant \(N\)-soft sets. Expert Syst Appl 115:95–105

    Google Scholar 

  51. Akram M, Adeel A, Alcantud JCR (2019) Hesitant fuzzy \(N\)-soft sets: A new model with applications in decision-making. J Intell Fuzzy Syst 36(6):6113–6127

    Google Scholar 

  52. Sebastian S, Ramakrishnan TV (2011) Multi-fuzzy sets: an extension of fuzzy sets. Fuzzy Inf Eng 1:35–43

    MathSciNet  MATH  Google Scholar 

  53. Yang Y, Tan X, Meng C (2013) The multi-fuzzy soft set and its application in decision making. Applied Mathematical Modelling 37:4915–4923

    MathSciNet  MATH  Google Scholar 

  54. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539

    MATH  Google Scholar 

  55. Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decisions. IEEE Int Conf Fuzzy Syst 1–3:1378–1382

    Google Scholar 

  56. Alcantud JCR, Torra V (2018) Decomposition theorems and extension principles for hesitant fuzzy sets. Inf Fus 41:48–56

    Google Scholar 

  57. Kar MB, Roy B, Kar S, Majumder S, Pamucar D (2019) Type-2 multi-fuzzy sets and their applications in decision making. Symmetry 11:170

    MATH  Google Scholar 

  58. Al-Qudah Y, Hassan N (2017) Operations on complex multi-fuzzy sets. J Intell Fuzzy Syst 33:1527–1540

    MATH  Google Scholar 

  59. Al-Qudah Y, Hassan N (2018) Complex multi-fuzzy soft set: its entropy and similarity measure. IEEE Access 6:65002–65017

    Google Scholar 

  60. Dey A, Pal M (2015) Generalised multi-fuzzy soft set and its application in decision making. Pacif Sci Rev A Nat Sci Eng 17(1):23–28

    Google Scholar 

  61. Akram M, Adeel A (2019) TOPSIS approach for MAGDM based on interval-valued hesitant fuzzy \(N\)-soft environment. Int J Fuzzy Syst 21(3):993–1009

    Google Scholar 

  62. Riaz M, Çagman N, Zareef I, Aslaam M (2019) \(N\)-soft topology and its applications to multi-criteria group decision making. J Intell Fuzzy Syst 36(6):6521–6536

    Google Scholar 

  63. Shabir M, Naz M (2011) On soft topological spaces. Comput Math Appl 61(7):1786–1799

    MathSciNet  MATH  Google Scholar 

  64. Terepeta M (2019) On separating axioms and similarity of soft topological spaces. Soft Comput 23(3):1049–1057

    MATH  Google Scholar 

  65. Alcantud JCR (2020) Soft open bases and a novel construction of soft topologies from bases for topologies. Mathematics 8(5):672

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to three anonymous referees for their valuable comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatia Fatimah.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of this research article.

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

Fatimah, F., Alcantud, J.C.R. The multi-fuzzy N-soft set and its applications to decision-making. Neural Comput & Applic 33, 11437–11446 (2021). https://doi.org/10.1007/s00521-020-05647-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05647-3

Keywords

Mathematics Subject Classification

Navigation