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A new efficient decision making algorithm based on interval-valued fuzzy soft set

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Abstract

Interval-valued fuzzy soft set is an extended model of soft set. It is a new mathematical tool that has great advantages in dealing with uncertain information and is proposed by combining soft sets and interval-valued fuzzy sets. The two existing fuzzy decision making algorithms based on interval-valued fuzzy soft sets were given. However, the two existing methods involve the high computational complexity and do not consider the added objects. In order to solve these problems, in this paper, we propose a new efficient decision making algorithm for interval-valued fuzzy soft sets. The comparison results among three methods on one real-life application and 30 synthetic generated datasets show that, the proposed algorithm involves relatively less computation and considers the added objects. Due to relatively less computation, our proposed algorithm has the much higher scalability for the large scale datasets compared with the two existing algorithms. Due to considering the added objects, our proposed algorithm has the much higher flexibility and is beneficial to the extension of interval-valued fuzzy soft set and combination of multiple interval-valued fuzzy soft sets.

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References

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

    MATH  Google Scholar 

  2. Bruno A, Simone B, Jacopo R, Gian LG, Matteo G, Luca F (2016) A neurofuzzy algorithm for learning from complex granules. Granular Comput 1:225–246

    Google Scholar 

  3. Han L, Mihaela C (2017) Fuzzy information granulation towards interpretable sentiment analysis. Granular Comput 2:289–302

    Google Scholar 

  4. Lai Y, Chen M, Chiang H (2018) Constructing the lie detection system with fuzzy reasoning approach. Granular Comput 3:169–176

    Google Scholar 

  5. Chiang H, Chen M, Wu Z (2018) Applying fuzzy petri nets for evaluating the impact of bedtime behaviors on sleep quality. Granular Comput 3:321–332

    Google Scholar 

  6. Liu H, Zhang L (2018) Fuzzy rule-based systems for recognition-intensive classification in granular computing context. Granular Comput 3:355–365

    Google Scholar 

  7. Molodtsov D (1999) Soft set theory_First results. Comput Math Appl 37(4/5):19–31

    MathSciNet  MATH  Google Scholar 

  8. Han B (2016) Comments on “Normal parameter reduction in soft set basedon particle swarm optimization algorithm”. Appl Math Model 40:10828–10834

    MathSciNet  MATH  Google Scholar 

  9. Maji PK, Roy AR (2002) An application of soft sets in a decision makingproblem. Comput Math Appl 44:1077–1083

    MathSciNet  MATH  Google Scholar 

  10. Chen D, Tsang ECC, Yeung DS, Wang X (2005) The parameterization reduction of soft sets and its applications. Comput Math Appl 49(5–6):757–763

    MathSciNet  MATH  Google Scholar 

  11. Ma X, Sulaiman N, Qin H, Herawan T, Zain JM (2011) A new efficient normal parameter reduction algorithm of soft sets. Comput Math Appl 62:588–598

    MathSciNet  MATH  Google Scholar 

  12. Han B, Li Y, Geng S (2017) 0–1 linear programming methods for optimal normal and pseudo parameter reductions of soft sets. Appl Soft Comput 54:467–484

    Google Scholar 

  13. Kong Z, Jia W, Zhang G, Wang L (2015) Normal parameter reduction in soft set based on particle swarm optimization algorithm. Appl Math Model 39(16):4808–4820

    MathSciNet  MATH  Google Scholar 

  14. Kong Z, Gao L, Wang L, Li S (2008) The normal parameter reduction of soft sets and its algorithm. Comput Math Appl 56(12):3029–3037

    MathSciNet  MATH  Google Scholar 

  15. Ma X, Qin H (2019) Soft set based parameter value reduction for decision making application. IEEE Access 7:35499–35511

    Google Scholar 

  16. Feng F, Cho J, Pedryczc W, Fujita H, Herawan T (2016) Soft set based association rule mining. Knowl-Based Syst 111:268–282

    Google Scholar 

  17. Herawan T, Mat Deris M (2011) A soft set approach for association rules mining. Knowl-Based Syst 24(1):186–195

    MATH  Google Scholar 

  18. Qin H, Ma X, Zain JM, Herawan T (2012) A novel soft set approach for selecting clustering attribute. Knowl-Based Syst 36:139–145

    Google Scholar 

  19. Zou Y, Xiao Z (2008) Data analysis approaches of soft sets under incomplete information. Knowl-Based Syst 21(8):941–945

    Google Scholar 

  20. Kong Z, Zhang G, Wang L, Wu Z, Qi S, Wang H (2014) An efficient decision making approach in incomplete soft set. Appl Math Model 38:2141–2150

    MathSciNet  MATH  Google Scholar 

  21. Qin H, Ma X, Herawan T, Zain JM (2012) DFIS: a novel data filling approach for an incomplete soft set. Int J Appl Math Comput Sci 22:817–828

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  23. Maji PK, Roy AR (2007) A fuzzy soft set theoretic approach to decision making problems. J Comput Appl Math 203:412–418

    MATH  Google Scholar 

  24. Ma X, Qin H (2018) A distance-based parameter reduction algorithm of fuzzy soft sets. IEEE Access 6:10530–10539

    Google Scholar 

  25. José CR (2016) Alcantud, “a novel algorithm for fuzzy soft set based decision making from multiobserver input parameter data set”. Inf Fusion 29:142–148

    Google Scholar 

  26. Maji PK, Biswas R, Roy AR (2001) Intuitionistic fuzzy soft sets. J Fuzzy Math 9(3):677–692

    MathSciNet  MATH  Google Scholar 

  27. Agarwal M, Biswas KK, Hanmandlu M (2013) Generalized intuitionistic fuzzy softsets with applications in decision-making. Appl Soft Comput 13:3552–3566

    Google Scholar 

  28. Das S, Kar S (2014) Group decision making in medical system: an intuitionistic fuzzy softset approach. Appl Soft Comput 24:196–211

    Google Scholar 

  29. Garg H, Arora R (2018) Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making. Appl Intell 48(2):343–356

    Google Scholar 

  30. Garg H, Arora R (2018) Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making. Oper Res Soc 69(11):1711–1724

    Google Scholar 

  31. Hu J, Pan L, Yang Y, Chen H (2019) A group medical diagnosis model based on intuitionistic fuzzy soft sets. Appl Soft Comput 77:453–466

    Google Scholar 

  32. Feng F, Fujita H, Ali MI, Yager RR, Liu X (2019) Another view on generalized intuitionistic fuzzy soft sets and related multiattribute decision making methods. IEEE Trans Fuzzy Syst 27(3):474–488

    Google Scholar 

  33. Rishu A, Harish G (2018) A robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment. Sci Iran 25(2):931–942

    Google Scholar 

  34. Jiang Y, Tang Y, Chen Q, Liu H, Tang J (2010) Interval-valued intuitionistic fuzzy soft sets and their properties. Comput Math Appl 60(3):906–918

    MathSciNet  MATH  Google Scholar 

  35. Harish G, Rishu A (2018) A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information. Appl Intell 48:2031–2046

    Google Scholar 

  36. Deli I, Cagman N (2015) Intuitionistic fuzzy parameterized soft set theory and itsdecision making. Appl Soft Comput 28:109–113

    Google Scholar 

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

    Google Scholar 

  38. Liu Y, Rosa MR, José CRA, Qin K, Luis M (2019) Hesitant linguistic expression soft sets: application to group decision making. Comput Ind Eng 136:575–590

    Google Scholar 

  39. Gong K, Wang P, Peng Y (2017) Fault-tolerant enhanced bijective soft set with applications. Appl Soft Comput 54:431–439

    Google Scholar 

  40. Muhammad A, Arooj A, CRA J (2019) Group decision-making methods based on hesitant N-soft sets. Expert Syst Appl 115:95–105

    Google Scholar 

  41. Pandey A, Kumar A (2017) A note on “A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets”. Appl Math Model 41:691–693

    MathSciNet  MATH  Google Scholar 

  42. Jianming Z, Qi L, Tutut H (2017) A novel soft rough set: soft rough hemirings and its multicriteria group decision making. Appl Soft Comput 54:393–402

    Google Scholar 

  43. Jianming Z, Muhammad IA, Nayyar M (2017) On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods. Appl Soft Comput 56:446–457

    Google Scholar 

  44. Jianming Z, Kuanyun Z (2017) A novel soft rough fuzzy set: Z-soft rough fuzzy ideals of hemirings and corresponding decision making. Soft Comput 21:1923–1936

    MATH  Google Scholar 

  45. Vijayabalaji S, Ramesh A (2019) Belief interval-valued soft set. Expert Syst Appl 119:262–271

    Google Scholar 

  46. Aggarwal M (2019) Confidence soft sets and applications in supplierselection. Comput Ind Eng 127:614–624

    Google Scholar 

  47. Chen SM, Chen JH (2009) Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators. Expert Syst Appl 36(30):6309–6317

    Google Scholar 

  48. Yang X, Lin TY, Yang J, Dongjun YLA (2009) Combination of interval-valued fuzzy set and soft set. Comput Math Appl 58:521–527

    MathSciNet  MATH  Google Scholar 

  49. Jiang Y, Tang Y, Liu H, Chen Z (2013) Entropy on intuitionistic fuzzy soft sets and on interval-valued fuzzy soft sets. Inf Sci 240:95–114

    MathSciNet  MATH  Google Scholar 

  50. Feng F, Li YM, Leoreanu-Fotea V (2010) Application of level soft sets in decision making based on interval-valued fuzzy soft sets. Comput Math Appl 60(6):1756–1767

    MathSciNet  MATH  Google Scholar 

  51. Peng X, 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 

  52. Qin H, Ma X (2019) Data analysis approaches of interval-valued fuzzy soft sets under incomplete information. IEEE Access 7:3561–3571

    Google Scholar 

  53. Qin H, Ma X (2018) A complete model for evaluation system based on interval-valued fuzzy soft set. IEEE Access 6:35012–35028

    Google Scholar 

  54. Ma X, Qin H, Sulaiman N, Herawan T, Abawajy J (2013) The parameter reduction of the interval-valued fuzzy soft sets and its related algorithms. IEEE Trans Fuzzy Syst 99:1

    Google Scholar 

  55. Peng X, Harish G (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 

  56. Chen SM, Chang YC, Pan JS (2013) Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. IEEE Trans Fuzzy Syst 21(3):412–425

    Google Scholar 

  57. Chen SM, Hsiao WH, Jong WT (1997) Bidirectional approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 91(3):339–353

    MathSciNet  MATH  Google Scholar 

  58. Chen SM, Hsiao WH (2000) Bidirectional approximate reasoning for rule-based systems using interval-valued fuzzy sets. Fuzzy Sets Syst 113(2):185–203

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation of China (No. 61662067, 61662068, 61762081).

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Ma, X., Fei, Q., Qin, H. et al. A new efficient decision making algorithm based on interval-valued fuzzy soft set. Appl Intell 51, 3226–3240 (2021). https://doi.org/10.1007/s10489-020-01915-w

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