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Similarity measures for type-2 fuzzy sets and application in MCDM

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Abstract

Type-2 fuzzy set theory is extensively used for decision making, pattern recognition and word computing due to exceptional expression of uncertain information. Similarity measure is one of the core tools for the application of interval and general type-2 fuzzy set. However, there are many similarities exist for interval type-2 fuzzy set, but very few for general type-2 fuzzy set, and the existing similarity measures have some limitations due to their dependence on certain representation, such as one of \(\alpha\)-plane, zSlices and vertical slice. To solve this problem, in this paper, Dice similarity and Cosine similarity for interval and general type-2 fuzzy set are proposed. The proposed similarity measures for general type-2 fuzzy set are defined on the basis of vector similarity; therefore, they do not depend on certain representation. Furthermore, weighted Dice and Cosine similarity measures are proposed for dealing with special situations in this work. Several properties and a discussion are exposed for proving the presented similarities are indeed similarity measures and can obtain reasonable similarity results. Ultimately, based on presented similarity measures, a multi-criteria decision-making method in the case that criteria weights are completely unknown is proposed. Several examples are employed for illustrating the rationality and superiority of presented similarity measures and MCDM method.

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References

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

    Article  MathSciNet  MATH  Google Scholar 

  2. Xiao F (2018) A novel multi-criteria decision making method for assessing health-care waste treatment technologies based on D numbers. Eng Appl Artif Intell 71(2018):216–225

    Article  Google Scholar 

  3. Jiang W, Huang K, Geng J, Deng X (2020) Multi-Scale Metric Learning for Few-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2020.2995754

  4. Jiang W, Cao Y, Deng X (2019) A novel z-network model based on bayesian network and z-number. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2019.2918999

  5. Jiang W (2018) A correlation coefficient for belief functions. Int J Approx Reason 103:94–106

    Article  MathSciNet  MATH  Google Scholar 

  6. Deng X, Jiang W (2019) Evaluating green supply chain management practices under fuzzy environment: a novel method based on D number theory. Int J Fuzzy Syst 21(5):1389–1402

    Article  Google Scholar 

  7. Hagras H (2007) Type-2 flcs: a new generation of fuzzy controllers. IEEE Comput Intell Mag 2(1):30–43

    Article  Google Scholar 

  8. Wu D, Tan WW (2006) Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Eng Appl Artif Intell 19(8):829–841

    Article  Google Scholar 

  9. Deng X, Jiang W (2019) D number theory based game-theoretic framework in adversarial decision making under a fuzzy environment. Int J Approx Reason 106:194–213

    Article  MathSciNet  MATH  Google Scholar 

  10. Kang B, Deng Y, Hewage K, Sadiq R (2019) A method of measuring uncertainty for Z-number. IEEE Trans Fuzzy Syst 27(4):731–738. https://doi.org/10.1109/TFUZZ.2018.2868496

    Article  Google Scholar 

  11. Luo Z, Deng Y (2019) A matrix method of basic belief assignment’s negation in dempster-shafer theory. IEEE Transactions on Fuzzy Systems 27. https://doi.org/10.1109/TFUZZ.2019.2930027

  12. Fei L, Deng Y (2019) Multi-criteria decision making in pythagorean fuzzy environment. Appl Intell 50:537–561. https://doi.org/10.1007/s10489-019-01532-2

    Article  Google Scholar 

  13. Xiao F, Ding W (2019) Divergence measure of pythagorean fuzzy sets and its application in medical diagnosis. Appli Soft Comput 79:254–267

    Article  Google Scholar 

  14. Zhai D, Mendel JM (2011) Uncertainty measures for general type-2 fuzzy sets. Inf Sci 181(3):503–518

    Article  MathSciNet  MATH  Google Scholar 

  15. Bo X, Lam HK, Li H (2017) Stabilization of interval type-2 polynomial-fuzzy-model-based control systems. Neurocomputing 25(1):205–217

    Google Scholar 

  16. Zhang X, Mahadevan S (2017) A game theoretic approach to network reliability assessment. IEEE Trans Reliab 66(3):875–892

    Article  Google Scholar 

  17. Mendel JM (2007) Type-2 fuzzy sets and systems: an overview. Comput Intell Mag IEEE 2(2):20–29

    Article  Google Scholar 

  18. Mendel JM, Liu F, Zhai D (2009) \(\alpha\)-plane representation for type-2 fuzzy sets: theory and applications. IEEE Trans Fuzzy Syst 17(5):1189–1207

    Article  Google Scholar 

  19. Mendel JM, Wu H (2006) Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: part 1, forward problems. IEEE Trans Fuzzy Syst 14(6):781–792

    Article  Google Scholar 

  20. Mendel JM, Wu H (2007) Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: part 2, inverse problems. IEEE Trans Fuzzy Syst 15(2):301–308

    Article  Google Scholar 

  21. Wagner C, Hagras H (2010) Toward general type-2 fuzzy logic systems based on z-slices. IEEE Trans Fuzzy Syst 18(4):637–660

    Article  Google Scholar 

  22. Zhang Z (2018) Trapezoidal interval type-2 fuzzy aggregation operators and their application to multiple attribute group decision making. Neural Comput Appl 29(4):1039–1054

    Article  Google Scholar 

  23. Zhang X, Mahadevan S, Sankararaman S, Goebel K (2018) Resilience-based network design under uncertainty. Reliab Eng Syst Safety 169:364–379

    Article  Google Scholar 

  24. Liu X (1992) Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Set Syst 52(3):305–318

    Article  MathSciNet  MATH  Google Scholar 

  25. Li WS, Priya ML (2000) Similarity-based ranking and query processing in multimedia databases. Elsevier, Amsterdam

    MATH  Google Scholar 

  26. Wei SH, Chen SM (2009) Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Syst Appl 36(3):6309–6317

    Article  Google Scholar 

  27. Chen SJ (2011) Measure of similarity between interval-valued fuzzy numbers for fuzzy recommendation process based on quadratic-mean operator. Expert Syst Appl 38(3):2386–2394

    Article  Google Scholar 

  28. Hesamian G (2017) Measuring similarity and ordering based on interval type-2 fuzzy numbers. IEEE Trans Fuzzy Syst 25(4):788–798

    Article  Google Scholar 

  29. Mitchell HB (2005) Pattern recognition using type-ii fuzzy sets. Inf Sci 170(2):409–418

    Article  Google Scholar 

  30. Hung WL, Yang MS (2004) Similarity measures between type-2 fuzzy sets. Int J Uncertain, Fuzziness Knowl-Based Syst 12(6):827–841

    Article  MathSciNet  MATH  Google Scholar 

  31. Wu D, Mendel JM (2019) Similarity measures for closed general type-2 fuzzy sets: overview, comparisons, and a geometric approach. IEEE Trans Fuzzy Syst 27(3):515–526

    Article  Google Scholar 

  32. Hamza MF, Yap HJ, Choudhury IA (2015) Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control. Neural Comput Appl 28(5):1–21

    Google Scholar 

  33. Abdullah L, Zulkifli N (2018) A new dematel method based on interval type-2 fuzzy sets for developing causal relationship of knowledge management criteria. Neural Comput Appl 31(5):1–17

    Google Scholar 

  34. Sevastjanov P, Figat P (2005) Aggregation of aggregating modes in mcdm: synthesis of type 2 and level 2 fuzzy sets. Omega 35(5):505–523

    Article  Google Scholar 

  35. Liu HC (2010) Type 2 generalized intuitionistic fuzzy choquet integral operator for multi-criteria decision making. International Symp Parallel and Distrib Process with Appl 46:605–611

    Google Scholar 

  36. Chen SM, Lee LW (2010) Fuzzy multiple attributes group decision-making based on the interval type-2 topsis method. Expert Syst Appl 37(4):2790–2798

    Article  Google Scholar 

  37. Chen SM, Lee LW (2010) Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Syst Appl 37(1):824–833

    Article  Google Scholar 

  38. Akay D, Kulak O, Henson B (2011) Conceptual design evaluation using interval type-2 fuzzy information axiom. Computer Ind 62(2):138–146

    Article  Google Scholar 

  39. Choi BI, Rhee FCH (2009) Interval type-2 fuzzy membership function generation methods for pattern recognition. Inf Sci 179(2):2102–2122

    Article  MATH  Google Scholar 

  40. Jimenez S, Gonzalez FA, Gelbukh A (2016) Mathematical properties of soft cardinality: enhancing jaccard, dice and cosine similarity measures with element-wise distance. Inf Sci 367:373–389

    Article  MATH  Google Scholar 

  41. Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathem Computer Modell 53(2):91–97

    Article  MathSciNet  MATH  Google Scholar 

  42. Zadeh AL (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8(3):199–249

    Article  MathSciNet  MATH  Google Scholar 

  43. Mj M, Jri B (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  44. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Article  Google Scholar 

  45. Bustince H (2000) Indicator of inclusion grade for interval-valued fuzzy sets. application to approximate reasoning based on interval-valued fuzzy sets. Int J Approx Reason 23(3):137–209

    Article  MathSciNet  MATH  Google Scholar 

  46. Cherif S, Baklouti N, Snasel V, Alimi AM (2017) New fuzzy similarity measures: From intuitionistic to type-2 fuzzy sets. In: IEEE International Conference on Fuzzy Systems, pp. 1–6. IEEE. https://doi.org/10.1109/FUZZ-IEEE.2017.8015696

  47. Gorzalzany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Syst 21(1):1–17

    Article  MathSciNet  Google Scholar 

  48. Wu D, Mendel JM (2010) Perceptual reasoning for perceptual computing: a similarity-based approach. Fuzzy Syst, IEEE Trans 17(6):1397–1411

    Google Scholar 

  49. Zeng W, Li H (2006) Relationship between similarity measure and entropy of interval valued fuzzy sets. Fuzzy Syst Math 157(11):1477–1484

    Article  MathSciNet  MATH  Google Scholar 

  50. Yang MS, Lin DC (2009) On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering. Computer Mathem Appl 57(6):896–907

    MathSciNet  MATH  Google Scholar 

  51. Mcculloch J, Wagner C, Aickelin U (2013) Extending similarity measures of interval type-2 fuzzy sets to general type-2 fuzzy sets. In: IEEE International Conference on Fuzzy Systems, pp. 1–8. IEEE

  52. Zhao T, Xiao J, Li YX, Deng XS (2014) A new approach to similarity and inclusion measures between general type-2 fuzzy sets. Soft Comput 18(4):809–823

    Article  MATH  Google Scholar 

  53. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  54. Mendel MJ (2001) Uncertain Rule-Based Fuzzy Systems. Pearson Higher Isia Education

  55. Wang J, Han Z (2013) Multi-criteria decision-making method based on triangular type-2 induced owa operator. Control and Decis 28(7):1037–796

    MATH  Google Scholar 

  56. Xie BK, Lee SJ (2016) An extended type-reduction method for general type-2 fuzzy sets. IEEE Trans Fuzzy Syst 25(3):1–1

    Google Scholar 

  57. Chiclana F, Zhou SM (2013) Type-reduction of general type-2 fuzzy sets: the type-1 owa approach. Int J Intell Syst 28(5):505–522

    Article  Google Scholar 

Download references

Acknowledgements

The work is partially supported by National Natural Science Foundation of China (Program No. 61703338) and National Science and Technology Major Project(2017-V-0011-0062).

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Correspondence to Wen Jiang.

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Jiang, W., Zhong, Y. & Deng, X. Similarity measures for type-2 fuzzy sets and application in MCDM. Neural Comput & Applic 33, 9481–9502 (2021). https://doi.org/10.1007/s00521-021-05707-2

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