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A novel decision making approach based on intuitionistic fuzzy soft sets

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Abstract

Molodtsov’s soft set was initiated as a general emerging mathematical tool to deal with uncertain problems, which is free from the limitations of other traditional mathematical tool. It has been proven that decision making based on soft sets boom in recent years in many different fields. In this paper, a novel multi-criteria ranking approach is generalized based on intuitionistic fuzzy soft sets. There will be only one optimal decision among all the selections, instead of several or all by this method. Firstly, we present several notations named degree-hesitation function, score function and accuracy function to intuitionistic fuzzy soft set, and then give several principles based on these concepts. Some different decision making algorithms can be got for different preference, and a concrete algorithm is proposed in a certain condition. Moreover, we introduced the weighted ranking approach to the weighted intuitionistic fuzzy soft set. At the same time, both of these situations are proved to be effective with the help of examples. Finally, we conclude the research and further research directions.

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References

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

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  4. Ruixia Yan, Jianguo Zheng, Jinliang Liu, Yuming Zhai (2010) Research on the model of rough set over dual-universes. Knowl-Based Syst 23:817–822

    Article  Google Scholar 

  5. Gau WL, Buehrer DJ (1993) Vague sets. IEEE Trans Syst Man Cabernet 23(2):610–614

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Sun BZ, Ma WM, Chen DG (2014) Rough approximation of a fuzzy concept on a hybrid attribute information system and its uncertainty measure. Inf Sci 284:180–196

    Article  MathSciNet  MATH  Google Scholar 

  8. Sun BZ, Ma WM, Zhao HY (2015) Decision-theoretic rough fuzzy set model and application. Inf Sci 283:180–196

    Article  MathSciNet  MATH  Google Scholar 

  9. Wang R, Kwon S, Wang XZ, Jiang QS (2015) Segment based decision tree induction with continuous valued attributes. IEEE Trans Cybernet 45(7):1262–1275

    Article  Google Scholar 

  10. Lu SX, Wang XZ, Zhang GQ, Zhou X (2015) Effective algorithms of the Moore–Penrose inverse matrices for extreme learning machine. Intell Data Anal 19(4):743–760

    Article  Google Scholar 

  11. Wang XZ, He Q, Chen DG, Yeung D (2005) A genetic algorithm for solving the inverse problem of support vector machines. Neurocomputing 68:225–238

    Article  Google Scholar 

  12. Wang XZ, Hong JR (1998) On the handling of fuzziness for continuous-valued attributes in decision tree generation. Fuzzy Sets Syst 99(3):283–290

    Article  MathSciNet  MATH  Google Scholar 

  13. Wang XZ, Xing HJ, Li Y et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  14. Wang XZ (2015) Learning from big data with uncertainty–editorial. J Intell Fuzzy Syst 28(5):2329–2330

    Article  MathSciNet  Google Scholar 

  15. Wang XZ, Aamir R, Fu A-M (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29:1185–1196

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  19. Aktas H, Cagman N (2007) Soft sets and soft groups. Inf Sci 177:2726–2735

    Article  MathSciNet  MATH  Google Scholar 

  20. Jun YB (2008) Soft BCK/BCI-algebras. Comput Math Appl 56:1408–1413

    Article  MathSciNet  MATH  Google Scholar 

  21. Jun YB, Park CH (2008) Applications of soft sets in ideal theory of BCK/BCI-algebras. Inf Sci 178:2466–2475

    MathSciNet  MATH  Google Scholar 

  22. Feng F, Jun YB, Zhao XZ (2008) Soft semirings. Comput Math Appl 56:2621–2628

    Article  MathSciNet  MATH  Google Scholar 

  23. Majumdar P, Samanta SK (2010) Generalised fuzzy soft sets. Comput Math Appl 58:1279–1286

    MathSciNet  MATH  Google Scholar 

  24. Xu W, Ma J, Wang S, Hao G (2010) Vague soft sets and their properties. Comput Math Appl 177:2726–2735

    MathSciNet  MATH  Google Scholar 

  25. Yang XB, Lin TY, Yang J, Li Y, Yu D (2009) Combination of interval-valued fuzzy set and soft set. Comput Math Appl 58:521–527

    Article  MathSciNet  MATH  Google Scholar 

  26. Jiang Y, Tang Y, Chen Q (2011) An adjustable approach to intuitionistic fuzzy soft sets based decision making. Appl Math Model 35:824–836

    Article  MathSciNet  MATH  Google Scholar 

  27. Feng F, Liu XY, Leoreanu-Fotea V, Jun YB (2011) Soft sets and soft rough sets. Inf Sci 181:1125–1137

    Article  MathSciNet  MATH  Google Scholar 

  28. Jun YB, Lee KJ, Khan A (2010) Soft ordered semigroups. Math Logic Q 56:42–50

    Article  MathSciNet  MATH  Google Scholar 

  29. Sun BZ, Ma WM (2014) Soft fuzzy rough sets and its application in decision making. Artif Intell Rev 41:67–80

    Article  Google Scholar 

  30. 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

    Article  MathSciNet  MATH  Google Scholar 

  31. Zou Y, Xiao Z (2008) Data analysis approaches of soft sets under incomplete information. Knowl-Based Syst 59:2128–2137

    Google Scholar 

  32. Herawan T, Dens MM (2011) A soft set approach for association rules mining. Knowl-Based Syst 24:186–195

    Article  Google Scholar 

  33. Xiao Z, Gong K, Zou Y (2009) A combined forecasting approach based on fuzzy soft sets. J Comput Appl Math 16:49–54

    MathSciNet  MATH  Google Scholar 

  34. Jiang YC, Liu H, Tang Y, Chen QM (2011) Semantic decision making using ontology-based soft sets. Math Comput Model 53:1140–1149

    Article  MathSciNet  MATH  Google Scholar 

  35. Roy AR, Maji PK (2007) A fuzzy soft set theoretic approach to decision making problems. J Comput Appl Math 203(2):412–418

    Article  MATH  Google Scholar 

  36. Mushrif MM, Sengupta S, Ray AK (2006) Texture classification using a novel, soft-set theory based classification algorithm. In: 7th Asian conference on computer vision, Hyderabad, India, pp 246–254

  37. Xiao Z, Li D, Gong K (2010) A bijective soft set approach to regional sustainable development evaluation. In: 1st international conference on sustainable construction and risk management. Chongqing Jiaotong University, Chongqing, Peoples Repubic of China, pp 1084–1089

  38. Cagman N, Enginoglu S (2010) Soft matrix theory and its decision making. Comput Math Appl 59(10):3308–3314

    Article  MathSciNet  MATH  Google Scholar 

  39. Cagman N, Enginoglu S (2010) Soft set theory and uni-int decision making. Eur J Oper Res 207(2):848–855

    Article  MathSciNet  MATH  Google Scholar 

  40. Feng F, Li Y, Cağman N (2012) Generalized uniint decision making schemes based on choice value soft sets. Eur J Oper Res 220:162–170

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  42. 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

    Article  MathSciNet  MATH  Google Scholar 

  43. Kong Z, Gao L, Wang L (2009) Comment on “A fuzzy soft set theoretic approach to decision making problems”. J Comput Appl Math 223(2):540–542

    Article  MATH  Google Scholar 

  44. Feng F, Jun YB, Liu X, Li L (2010) An adjustable approach to fuzzy soft set based decision making. J Comput Appl Math 234(1):10–20

    Article  MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  46. Maji PK, Roy AR, Biswas R (2004) On intuitionistic fuzzy soft sets. J Fuzzy Math 12(3):669–683

    MathSciNet  MATH  Google Scholar 

  47. Chen SM, Tan JM (1994) Handling multi-criteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst 67:163–172

    Article  MATH  Google Scholar 

  48. Li Z, Wen G, Han Y (2014) Decision making based on intuitionistic fuzzy soft sets and its algorithm. J Comput Anal Appl 17(4):620–631

    MathSciNet  MATH  Google Scholar 

  49. 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

    Article  MathSciNet  Google Scholar 

  50. Hong DH, Choi CH (2000) Multi-criteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst 114:103–113

    Article  MATH  Google Scholar 

  51. Wang JQ, Li KJ, Zhang HY (2012) Interval-valued intuitionistic fuzzy multi-criteria decision-making approach based on prospect score function. Knowl-Based Syst 27:119–125

    Article  Google Scholar 

Download references

Acknowledgments

The authors are very grateful to the Editor in Chief Professor Xi-Zhao Wang, and the three anonymous referees for their thoughtful improvement on the manuscript. The work was partly supported by the National Natural Science Foundation of China (71571090,71161016), the Foundation Research Funds for the Central Universities (JB150605), the Chinese Postdoctoral Science Foundation (XJS15067).

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Correspondence to Bingzhen Sun.

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Zhao, H., Ma, W. & Sun, B. A novel decision making approach based on intuitionistic fuzzy soft sets. Int. J. Mach. Learn. & Cyber. 8, 1107–1117 (2017). https://doi.org/10.1007/s13042-015-0481-z

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  • DOI: https://doi.org/10.1007/s13042-015-0481-z

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