Parameter Reduction Method for Pythagorean Fuzzy Soft Sets

Year 2020, Volume: 3 Issue: 1, 33 - 37, 25.03.2020

Murat Kirisci

https://doi.org/10.32323/ujma.615166

Cited By: 1

Abstract

The aim of this paper is to give new parameter reduction methods according to Pythagorean fuzzy soft sets. The reason for the definition of these methods is to help decision-makers facilitate their decision-making processes. The algorithm of the first method defined is related to the selection of some parameters. The second method determines the parameters with less deviation than the other parameters. Further, numerical examples related to the new algorithms are examined.

Keywords

Parameter reduction, Pythagorean fuzzy soft set.

References

• [1] L.A. Zadeh, Fuzzy sets, Inf. Comp., 8, (1965), 338–353.
• [2] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, (1986), 87–96.
• [3] D. Molodtsov, Soft Set Theory-First Results, Comput. Math. Appl., 37, (1999), 19–31.
• [4] R. R. Yager, Pythagorean fuzzy subsets, In: Proc Joint IFSA World Congress and NAFIPS Annual M eeting, Edmonton, Canada; (2013), 57–61.
• [5] R. R. Yager, Pythagorean membership grades in multicriteria decision making, IEEE Trans Fuzzy Syst., 22, (2014), 958–965.
• [6] R.R. Yager, Properties and applications of Pyhagorean fuzzy sets, in Imprecision and Uncertainty in Information Representation and Processing, Springer, Berlin, (2016), 119–136.
• [7] X. Peng, Y. Yang, J. Song, Y. Jiang, Pythagorean fuzzy soft set and its application, Computer Engineering, 41(7), (2015), 224–229.
• [8] M. Kiris¸ci, New type Pythagorean fuzzy soft set and decision-making application, https://arxiv.org/pdf/1904.04064.pdf.
• [9] XL Zhang , ZS Xu, Extension of TOPSIS to multi-criteria decision making with Pythagorean fuzzy sets, Int. J. Intell. Syst., 29, (2014), 1061–1078.
• [10] P. K. Maji, R. Biswas, A. R. Roy, Intuitionistic fuzzy soft sets, The journal of fuzzy mathematics, 9, (2001), 677–692.
• [11] S. Broumi, Q-Intuitionistic fuzzy soft sets, Journal of New Theory, 5, (2015), 80-91.
• [12] S. Broumi, F. Smarandache, M. Dhar, P. Majumdar, New results of intuitionistic fuzzy soft set, I.J. Information Engineering and Electronic Business, 2, (2014), 47–52.
• [13] S. Broumi, P. Majumdar, F. Smarandache, New Operations on Intuitionistic Fuzzy Soft Sets Based on First Zadeh’s Logical Operators, Journal of New Results in Sciences, 4, (2014), 71–81.
• [14] S. Broumi, P. Majumdar, F. Smarandache, New Operations on Intuitionistic Fuzzy Soft Sets based on Second Zadeh’s logical Operators, 5, (2015), 80–91.
• [15] P.K. Maji, More on Intuitionistic Fuzzy Soft Sets. In: Sakai H., Chakraborty M.K., Hassanien A.E., Slezak D., Zhu W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science, vol 5908. Springer, Berlin, Heidelberg, (2009).
• [16] Z. Zhang, A rough set approach to intuitionistic fuzzy soft set based decision making, Applied Mathematical Modelling, 36, (2012), 44605–4633.
• [17] S. Ghsoh, S. Das, Parameter reduction of intuitionistic fuzzy soft sets and its related algorithms, Proceedings of the 4th International Conference on Frontiers in intelligent computing: Theory and Applications(FICTA) (2015).
• [18] M. Kirişçi, Comparison the medical decision-making with Intuitionistic fuzzy parameterized fuzzy soft set and Riesz Summability, New Mathematics and Natural Computation, 15(2), (2019), 351–359. doi: 10.1142/S1793005719500194.
• [19] Yager RR, Abbasov AM. Pythagorean membership grades, complex numbers, and decision making. Int J Intell Syst 2014;28:436–452.
• [20] H. Garg, A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making, Int J Intell Syst, 31, (2016), 886–920.