Research article

Three-way decision based on canonical soft sets of hesitant fuzzy sets

  • Received: 28 July 2021 Accepted: 29 October 2021 Published: 08 November 2021
  • MSC : 03E72, 62A86, 68T35, 90B50, 06D72, 94D05

  • The theory of three-way decision is built on the philosophy of thinking in threes. The essence of three-way decision is trisecting the whole and taking different strategies for different parts accordingly. The theory of three-way decision has been successfully implemented to diverse fields since it provides an elegant and efficient solution for solving complicated problems. In this paper, a useful representation for hesitant fuzzy sets is obtained by means of canonical soft sets. We also define unit interval parameterized soft sets and their derived hesitant fuzzy sets. Mutual representations and inner connections between hesitant fuzzy sets and soft sets are examined. With the help of soft rough sets, a generalized rough model based on hesitant fuzzy sets is established. A novel three-way decision method is presented for solving decision-making problems by means of hesitant fuzzy sets and canonical soft sets. Finally, a numerical example regarding peer review of research articles is given to illustrate the validity and efficacy of the proposed method.

    Citation: Feng Feng, Zhe Wan, José Carlos R. Alcantud, Harish Garg. Three-way decision based on canonical soft sets of hesitant fuzzy sets[J]. AIMS Mathematics, 2022, 7(2): 2061-2083. doi: 10.3934/math.2022118

    Related Papers:

  • The theory of three-way decision is built on the philosophy of thinking in threes. The essence of three-way decision is trisecting the whole and taking different strategies for different parts accordingly. The theory of three-way decision has been successfully implemented to diverse fields since it provides an elegant and efficient solution for solving complicated problems. In this paper, a useful representation for hesitant fuzzy sets is obtained by means of canonical soft sets. We also define unit interval parameterized soft sets and their derived hesitant fuzzy sets. Mutual representations and inner connections between hesitant fuzzy sets and soft sets are examined. With the help of soft rough sets, a generalized rough model based on hesitant fuzzy sets is established. A novel three-way decision method is presented for solving decision-making problems by means of hesitant fuzzy sets and canonical soft sets. Finally, a numerical example regarding peer review of research articles is given to illustrate the validity and efficacy of the proposed method.



    加载中


    [1] M. Agarwal, K. K. Biswas, M. Hanmandlu, Generalized intuitionistic fuzzy soft sets with applications in decision-making, Appl. Soft Comput., 13 (2013), 3552–3566. doi: 10.1016/j.asoc.2013.03.015. doi: 10.1016/j.asoc.2013.03.015
    [2] J. C. R. Alcantud, A. Laruelle, Dis & approval voting: A characterization, Soc. Choice Welf., 43 (2014), 1–10. doi: 10.1007/s00355-013-0766-7. doi: 10.1007/s00355-013-0766-7
    [3] J. C. R. Alcantud, V. Torra, Decomposition theorems and extension principles for hesitant fuzzy sets, Inf. Fusion, 41 (2018), 48–56. doi: 10.1016/j.inffus.2017.08.005. doi: 10.1016/j.inffus.2017.08.005
    [4] M. I. Ali, F. Feng, X. Y. Liu, W. K. Min, M. Shabir, On some new operations in soft set theory, Comput. Math. Appl., 57 (2009), 1547–1553. doi: 10.1016/j.camwa.2008.11.009. doi: 10.1016/j.camwa.2008.11.009
    [5] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20 (1986), 87–96.
    [6] T. M. Athira, J. J. Sunil, H. Garg, A novel entropy measure of Pythagorean fuzzy soft sets, AIMS Math., 5 (2020), 1050–1061. doi: 10.3934/math.20200073. doi: 10.3934/math.20200073
    [7] B. Bedregal, R. Reiser, H. Bustince, C. López-Molina, V. Torra, Aggregation functions for typical hesitant fuzzy elements and the action of automorphisms, Inf. Sci., 255 (2014), 82–99. doi: 10.1016/j.ins.2013.08.024. doi: 10.1016/j.ins.2013.08.024
    [8] D. G. Chen, E. C. C. Tsang, D. S. Yeung, X. Z. Wang, The parameterization reduction of soft sets and its application, Comput. Math. Appl., 49 (2005), 757–763. doi: 10.1016/j.camwa.2004.10.036. doi: 10.1016/j.camwa.2004.10.036
    [9] X. Deng, Y. Yao, Decision-theoretic three-way approximations of fuzzy sets, Inf. Sci., 279 (2014), 702–715. doi: 10.1016/j.ins.2014.04.022. doi: 10.1016/j.ins.2014.04.022
    [10] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst., 17 (1990), 191–209. doi: 10.1080/03081079008935107. doi: 10.1080/03081079008935107
    [11] F. Fatimah, D. Rosadi, R. F. Hakim, J. C. R. Alcantud, Probabilistic soft sets and dual probabilistic soft sets in decision-making, Neural Comput. Applic., 31 (2019), 397–407. doi: 10.1007/s00521-017-3011-y. doi: 10.1007/s00521-017-3011-y
    [12] F. Feng, J. Cho, W. Pedrycz, H. Fujita, T. Herawan, Soft set based association rule mining, Knowl.-Based Syst., 111 (2016), 268–282. doi: 10.1016/j.knosys.2016.08.020. doi: 10.1016/j.knosys.2016.08.020
    [13] F. Feng, H. Fujita, M. I. Ali, R. R. Yager, X. Liu, Another view on generalized intuitionistic fuzzy soft sets and related multiattribute decision making methods, IEEE Trans. Fuzzy Syst., 27 (2019) 474-488. doi: 10.1109/TFUZZ.2018.2860967. doi: 10.1109/TFUZZ.2018.2860967
    [14] F. Feng, C. X. Li, B. Davvaz, M. I. Ali, Soft sets combined with fuzzy sets and rough sets: A tentative approach, Soft Comput., 14 (2010), 899–911. doi: 10.1007/s00500-009-0465-6. doi: 10.1007/s00500-009-0465-6
    [15] F. Feng, Y. M. Li, Soft subsets and soft product operations, Inf. Sci., 232 (2013) 44–57. doi: 10.1016/j.ins.2013.01.001. doi: 10.1016/j.ins.2013.01.001
    [16] F. Feng, X. Y. Liu, V. L. Fotea, Y. B. Jun, Soft sets and soft rough sets, Inf. Sci., 181 (2011), 1125–1137. doi: 10.1016/j.ins.2010.11.004. doi: 10.1016/j.ins.2010.11.004
    [17] F. Feng, Z. Xu, H. Fujita, M. Q. Liang, Enhancing PROMETHEE method with intuitionistic fuzzy soft sets, Int. J. Intell. Syst., 35 (2020), 1071–1104. doi: 10.1002/int.22235. doi: 10.1002/int.22235
    [18] H. Garg, R. Arora, Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making, Appl. Intell., 48 (2018), 343–356. doi: 10.1007/s10489-017-0981-5. doi: 10.1007/s10489-017-0981-5
    [19] H. Garg, R. Arora, TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information, AIMS Math., 5 (2020), 2944–2966. doi: 10.3934/math.2020190. doi: 10.3934/math.2020190
    [20] H. Garg, R. Arora, Generalized Maclaurin symmetric mean aggregation operators based on Archimedean t-norm of the intuitionistic fuzzy soft set information, Artif. Intell. Rev., 54 (2021), 3173–3213. doi: 10.1007/s10462-020-09925-3. doi: 10.1007/s10462-020-09925-3
    [21] J. Goguen, L-fuzzy sets, J. Math. Anal. Appl., 18 (1967), 145–147.
    [22] B. Q. Hu, Three-way decisions space and three-way decisions, Inf. Sci., 281 (2014), 21–52. doi: 10.1016/j.ins.2014.05.015. doi: 10.1016/j.ins.2014.05.015
    [23] B. Q. Hu, H. Wong, K. C. Yiu, On two novel types of three-way decisions in three-way decision spaces, Int. J. Approx. Reason., 82 (2017), 285–306. doi: 10.1016/j.ijar.2016.12.007. doi: 10.1016/j.ijar.2016.12.007
    [24] X. Y. Jia, Z. M. Tang, W. H. Liao, L. Shang, On an optimization representation of decision-theoretic rough set model, Int. J. Approx. Reason., 55 (2014), 156–166. doi: 10.1016/j.ijar.2013.02.010. doi: 10.1016/j.ijar.2013.02.010
    [25] A. Laruelle, "Not this one": Experimental use of the approval and disapproval ballot, Homo Oecon., 2021. doi: 10.1007/s41412-021-00110-7. doi: 10.1007/s41412-021-00110-7
    [26] X. N. Li, B. Z. Sun, Y. H. She, Generalized matroids based on three-way decision models, Int. J. Approx. Reason., 90 (2017), 21–52. doi: 10.1016/j.ijar.2017.07.012. doi: 10.1016/j.ijar.2017.07.012
    [27] X. N. Li, Q. Q. Sun, H. M. Chen, H. J. Yi, Three-way decision on two universes, Inf. Sci., 515 (2020), 263–279. doi: 10.1016/j.ins.2019.12.020. doi: 10.1016/j.ins.2019.12.020
    [28] D. C. Liang, D. Liu, A novel risk decision making based on decision-theoretic rough sets under hesitant fuzzy information, IEEE Trans. Fuzzy Syst., 23 (2015), 192–207. doi: 10.1109/TFUZZ.2014.2310495. doi: 10.1109/TFUZZ.2014.2310495
    [29] D. C. Liang, W. Pedrycz, D. Liu, P. Hu, Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making, Appl. Soft. Comput., 29 (2015), 256–269. doi: 10.1016/j.asoc.2015.01.008. doi: 10.1016/j.asoc.2015.01.008
    [30] P. D. Liu, Y. M. Wang, F. Jia, H. Fujita, A multiple attribute decision making three-way model for intuitionistic fuzzy numbers, Int. J. Approx. Reason., 119 (2020), 177–203. doi: 10.1016/j.ijar.2019.12.020. doi: 10.1016/j.ijar.2019.12.020
    [31] P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., 9 (2001), 589–602.
    [32] P. K. Maji, R. Biswas, A. R. Roy, Intuitionistic fuzzy soft sets, J. Fuzzy Math., 9 (2001), 677–692.
    [33] P. K. Maji, R. Biswas, A. R. Roy, Soft set throry, Comput. Math. Appl., 45 (2003) 555–562. doi: 10.1016/S0898-1221(03)00016-6.
    [34] P. K. Maji, A. R. Roy, R. Biswas, An application of soft sets in a decision making problem, Comput. Math. Appl., 44 (2002), 1077–1083.
    [35] P. Majumdar, S. K. Samanta, Generalised fuzzy soft sets, Comput. Math. Appl., 59 (2010), 1425–1432. doi: 10.1016/j.camwa.2009.12.006. doi: 10.1016/j.camwa.2009.12.006
    [36] D. A. Molodtsov, Soft set theory-first results, Comput. Math. Appl., 37 (1999), 19–31. doi: 10.1016/S0898-1221(99)00056-5. doi: 10.1016/S0898-1221(99)00056-5
    [37] Z. Pawlak, Rough sets, Int. J. Comput. Inf. Sci., 11 (1982), 341–356. doi: 10.1007/BF01001956. doi: 10.1007/BF01001956
    [38] X. D. Peng, Y. Yang, J. Song, Y. Jiang, Pythagoren fuzzy soft set and its application, Comput. Eng., 41 (2015), 224–229.
    [39] A. M. Raszikowaka, E. E. Kerre, A comparative study of fuzzy rough sets, Fuzzy Sets Syst., 126 (2002), 137–155. doi: 10.1016/S0165-0114(01)00032-X. doi: 10.1016/S0165-0114(01)00032-X
    [40] B. Z. Sun, W. Ma, Soft fuzzy rough sets and its application in decision making, Artif. Intell. Rev., 41 (2014), 67–80. doi: 10.1007/s10462-011-9298-7. doi: 10.1007/s10462-011-9298-7
    [41] V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst., 25 (2010), 529–539. doi: 10.1002/int.20418. doi: 10.1002/int.20418
    [42] I. B. Turksen, Interval valued fuzzy sets based on normal forms, Fuzzy Sets Syst., 20 (1986), 191–210. doi: 10.1016/0165-0114(86)90077-1. doi: 10.1016/0165-0114(86)90077-1
    [43] J. J. Wang, X. L. Ma, Z. S. Xu, J. M. Zhan, Three-way multi-attribute decision making under hesitant fuzzy environments, Inf. Sci., 552 (2021), 328–351. doi: 10.1016/j.ins.2020.12.005. doi: 10.1016/j.ins.2020.12.005
    [44] X. F. Wen, X. H. Zhang, T. Lei, Intuitionistic fuzzy (IF) overlap functions and IF-rough sets with applications, Symmetry, 13 (2021), 1494. doi: 10.3390/sym13081494. doi: 10.3390/sym13081494
    [45] M. M. Xia, Z. S. Xu, Hesitant fuzzy information aggregation in decision making, Int. J. Approx. Reason., 52 (2011), 395–407. doi: 10.1016/j.ijar.2010.09.002. doi: 10.1016/j.ijar.2010.09.002
    [46] T. Xie, Z. T. Gong, A hesitant soft fuzzy rough set and its applications, IEEE Access, 7 (2019), 167766–167783. doi: 10.1109/ACCESS.2019.2954179. doi: 10.1109/ACCESS.2019.2954179
    [47] J. L. Yang, Y. Y. Yao, Semantics of soft sets and three-way decision with soft sets, Knowl.-Based Syst., 194 (2020), 105538. doi: 10.1016/j.knosys.2020.105538. doi: 10.1016/j.knosys.2020.105538
    [48] Y. Y. Yao, Probabilistic approaches to rough sets, Expert Syst., 20 (2003), 287–297. doi: 10.1111/1468-0394.00253. doi: 10.1111/1468-0394.00253
    [49] Y. Y. Yao, Three-way decisions with probabilistic rough sets, Inf. Sci., 180 (2010), 341–353. doi: 10.1016/j.ins.2009.09.021. doi: 10.1016/j.ins.2009.09.021
    [50] Y. Y. Yao, The superiority of three-way decisions in probabilistic rough set models, Inf. Sci., 181 (2011), 1080–1096. doi: 10.1016/j.ins.2010.11.019. doi: 10.1016/j.ins.2010.11.019
    [51] Y. Y. Yao, An outline of a theory of three-way decisions, In: J. T. Yao, Y. Yang, R. Słowiński, S. Greco, H. X. Li, S. Mitra, L. Polkowski, Rough sets and current trends in computing, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 7413 (2012), 1–17. doi: 10.1007/978-3-642-32115-3_1.
    [52] Y. Y. Yao, Three-way decision and granular computing, Int. J. Approx. Reason., 103 (2018), 107–123. doi: 10.1016/j.ijar.2018.09.005. doi: 10.1016/j.ijar.2018.09.005
    [53] Y. Y. Yao, Tri-level thinking: Models of three-way decision, Int. J. Mach. Learn. Cyber., 11 (2020), 947–959. doi: 10.1007/s13042-019-01040-2. doi: 10.1007/s13042-019-01040-2
    [54] Y. Y. Yao, Set-theoretic models of three-way decision, Granul. Comput., 6 (2021), 133–148. doi: 10.1007/s41066-020-00211-9. doi: 10.1007/s41066-020-00211-9
    [55] H. Yu, Z. G. Liu, G. Y. Wang, An automatic method to determine the number of clusters using decision-theoretic rough set, Int. J. Approx. Reason., 55 (2013), 101–115. doi: 10.1016/j.ijar.2013.03.018. doi: 10.1016/j.ijar.2013.03.018
    [56] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. doi: 10.1016/S0019-9958(65)90241-X. doi: 10.1016/S0019-9958(65)90241-X
    [57] S. Zhang, Z. S. Xu, Y. He, Operations and integrations of probabilistic hesitant fuzzy information in decision making, Inf. Fusion, 38 (2017), 1–11. doi: 10.1016/j.inffus.2017.02.001. doi: 10.1016/j.inffus.2017.02.001
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2303) PDF downloads(113) Cited by(15)

Article outline

Figures and Tables

Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog