Voir la notice de l'article provenant de la source Library of Science
@article{IJAMCS_2022_32_4_a8, author = {Xia, Sisi and Chen, Lin and Liu, Siya and Yang, Haoran}, title = {A new method for decision making problems with redundant and incomplete information based on incomplete soft sets: {From} crisp to fuzzy}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {657--669}, publisher = {mathdoc}, volume = {32}, number = {4}, year = {2022}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_4_a8/} }
TY - JOUR AU - Xia, Sisi AU - Chen, Lin AU - Liu, Siya AU - Yang, Haoran TI - A new method for decision making problems with redundant and incomplete information based on incomplete soft sets: From crisp to fuzzy JO - International Journal of Applied Mathematics and Computer Science PY - 2022 SP - 657 EP - 669 VL - 32 IS - 4 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_4_a8/ LA - en ID - IJAMCS_2022_32_4_a8 ER -
%0 Journal Article %A Xia, Sisi %A Chen, Lin %A Liu, Siya %A Yang, Haoran %T A new method for decision making problems with redundant and incomplete information based on incomplete soft sets: From crisp to fuzzy %J International Journal of Applied Mathematics and Computer Science %D 2022 %P 657-669 %V 32 %N 4 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_4_a8/ %G en %F IJAMCS_2022_32_4_a8
Xia, Sisi; Chen, Lin; Liu, Siya; Yang, Haoran. A new method for decision making problems with redundant and incomplete information based on incomplete soft sets: From crisp to fuzzy. International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 4, pp. 657-669. http://geodesic.mathdoc.fr/item/IJAMCS_2022_32_4_a8/
[1] [1] Alcantud, J.C.R., Feng, F. and Yager, R.R. (2020). An n-soft set approach to rough sets, IEEE Transactions on Fuzzy Systems 28(11): 2996–3007.
[2] [2] Ali, M., Kilicman, A. and Khameneh, A.Z. (2020). Separation axioms of interval-valued fuzzy soft topology via quasi-neighborhood structure, Mathematics 8(2), Article no. 178.
[3] [3] Cagman, N. and Karatas, S. (2013). Intuitionistic fuzzy soft set theory and its decision making, Journal of Intelligent Fuzzy Systems 24(4): 829–836.
[4] [4] de Andres, J., Landajo, M. and Lorca, P. (2012). Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios, Knowledge-Based Systems 30: 67–77.
[5] [5] Deng, T. and Wang, X. (2013). An object-parameter approach to predicting unknown data in incomplete fuzzy soft sets, Applied Mathematical Modelling 37(6): 4139–4146.
[6] [6] Feng, F., Xu, Z., Fujita, H. and Liang, M. (2020). Enhancing PROMETHEE method with intuitionistic fuzzy soft sets, International Journal of Intelligent Systems 35(7): 1071–1104.
[7] [7] Garg, H. and Arora, R. (2018). Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making, Journal of the Operational Research Society 69(11): 1711–1724.
[8] [8] Gau, W.L. and Buehrer, D.J. (1993). Vague sets, IEEE Transactions on Systems, Man and Cybernetics 23(2): 610–614.
[9] [9] Hussain, A., Ali, M.I., Mahmood, T. and Munir, M. (2020). q-Rung orthopair fuzzy soft average aggregation operators and their application in multicriteria decision-making, International Journal of Intelligent Systems 35(4): 571–599.
[10] [10] Li, M.-Y., Fan, Z.-P. and You, T.-H. (2018). Screening alternatives considering different evaluation index sets: A method based on soft set theory, Applied Soft Computing 64: 614–626.
[11] [11] Li, Z., Wen, G. and Xie, N. (2015). An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster–Shafer theory of evidence: An application in medical diagnosis, Artificial Intelligence in Medicine 64(3): 161–71.
[12] [12] Liu, B. (2007). Uncertainty Theory, Springer, Berlin.
[13] [13] Liu, Y., Qin, K., Rao, C. and Alhaji Mahamadu, M. (2017). Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set, International Journal of Applied Mathematics and Computer Science 27(1): 157–167, DOI: 10.1515/amcs-2017-0011.
[14] [14] Maji, P.K. and Roy, A.R. (2002). An application of soft sets in a decision making problem, Computers Mathematics with Applications 44(8–9): 1077–1083.
[15] [15] Molodtsov, D. (1999). Soft set theory—First results, Computers Mathematics with Applications 37(4–5): 19–31.
[16] [16] Pawlak, Z. (1984). Rough classification, International Journal of Man-Machine Studies 20(5): 469–483.
[17] [17] Pawlak, Z. (1985). Rough sets and decision tables, in A. Skowron (Ed), Computation Theory: SCT 1984, Lecture Notes in Computer Science, Vol. 208, Springer, Berlin, pp. 187–196.
[18] [18] Peng, X. and 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, Applied Soft Computing 54: 415–430.
[19] [19] Qayyum, A. and Shaheen, T. (2020). Graded soft expert set as a generalization of hesitant fuzzy set, Journal of Intelligent Systems 29(1): 223–236.
[20] [20] Xia, S., Yang, H. and Chen, L. (2021). An incomplete soft set and its application in MCDM problems with redundant and incomplete information, International Journal of Applied Mathematics and Computer Science 31(3): 417–430, DOI: 10.34768/amcs-2021-0028.
[21] [21] Xiao, Z., Chen, W.J. and Li, L.L. (2013). A method based on interval-valued fuzzy soft set for multi-attribute group decision-making problems under uncertain environment, Knowledge and Information Systems 34(3): 653–669.
[22] [22] Xiao, Z., Xia, S.S., Gong, K. and Li, D. (2012). The trapezoidal fuzzy soft set and its application in MCDM, Applied Mathematical Modelling 36(12): 5844–5855.
[23] [23] Xu, W., Pan, Y., Chen, W. and Fu, H. (2019). Forecasting corporate failure in the Chinese energy sector: A novel integrated model of deep learning and support vector machine, Energies 12(12), Article no. 2251.
[24] [24] Xu, W., Xiao, Z., Dang, X., Yang, D. and Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory, Knowledge-Based Systems 63: 59–67.
[25] [25] Yang, J. and Yao, Y. (2020). Semantics of soft sets and three-way decision with soft sets, Knowledge-Based Systems 194, Article no. 105538.
[26] [26] Yang, Y., Tan, X. and Meng, C.C. (2013). The multi-fuzzy soft set and its application in decision making, Applied Mathematical Modelling 37(7): 4915–4923.
[27] [27] Zadeh, L.A. (1965). Fuzzy sets, Information and Control 8(3): 338–353.
[28] [28] Zhang, Z.M. (2012). A rough set approach to intuitionistic fuzzy soft set based decision making, Applied Mathematical Modelling 36(10): 4605–4633.
[29] [29] Zhang, Z.M. and Zhang, S.H. (2013). A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets, Applied Mathematical Modelling 37(7): 4948–4971.
[30] [30] Zou, Y. and Xiao, Z. (2008). Data analysis approaches of soft sets under incomplete information, Knowledge-Based Systems 21(8): 941–945.