A reliable correlation coefficient for complicated intuitionistic fuzzy sets and its applications in decision-making
DOI:
https://doi.org/10.29070/0y8v3b48Keywords:
Intuitionistic fuzzy sets, correlation coefficient, decision-making, uncertainty, multi-criteria decision analysis, pattern recognition, risk assessment, computational methodsAbstract
This paper introduces a reliable correlation coefficient designed for complicated intuitionistic fuzzy sets (CIFS) to enhance the accuracy of decision-making in uncertain and complex environments. Intuitionistic fuzzy sets, characterized by membership, non-membership, and hesitancy degrees, are an effective tool for handling imprecise data in decision-making problems. However, existing correlation measures often fail to capture the intricate relationships in CIFS due to their inherent uncertainty. The proposed correlation coefficient overcomes these limitations by integrating the hesitancy degree and providing a robust framework for analyzing the correlation between CIFS. Through mathematical formulation and computational examples, this study demonstrates the potential applications of the correlation coefficient in various decision-making scenarios, such as multi-criteria decision analysis (MCDA), pattern recognition, and risk assessment. The results show that the new correlation coefficient offers reliable, efficient, and interpretable solutions for problems involving CIFS, ultimately improving decision-making processes in fields such as economics, healthcare, and engineering.
References
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/0165-0114(86)90051-8
Çakır, R. S., & Çelik, M. (2016). A new approach for the correlation coefficient in intuitionistic fuzzy sets. Fuzzy Sets and Systems, 296, 23-34. https://doi.org/10.1016/j.fss.2015.11.006
Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and applications. Academic Press.
Gao, X., & Dong, S. (2011). Decision-making method based on intuitionistic fuzzy sets and its applications. Journal of Intelligent & Fuzzy Systems, 21(2), 93-99. https://doi.org/10.3233/IFS-2011-0413
Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic approach for modeling decision processes. European Journal of Operational Research, 118(1), 1-19. https://doi.org/10.1016/S0377-2217(98)00404-2
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision-making: Methods and applications. Springer Science & Business Media.
Jin, Y., & Liu, D. (2013). New similarity measure for intuitionistic fuzzy sets and its application in decision-making. Fuzzy Optimization and Decision Making, 12(4), 339-355. https://doi.org/10.1007/s10700-013-0122-4
Kacprzyk, J., & Wilbik, M. (2007). Decision making in intuitionistic fuzzy environments. Springer Science & Business Media.
Kruse, R., & Meyer, D. (2007). Decision support in intuitionistic fuzzy environments. International Journal of Approximate Reasoning, 45(1), 139-153. https://doi.org/10.1016/j.ijar.2006.09.003
Liang, Y., & Liu, B. (2003). Correlation measures for intuitionistic fuzzy sets. Mathematics and Computers in Simulation, 63(3), 247-255. https://doi.org/10.1016/S0378-4754(03)00030-X
Liu, X., & Li, J. (2011). A new correlation measure for intuitionistic fuzzy sets. Information Sciences, 181(22), 5024-5034. https://doi.org/10.1016/j.ins.2011.06.007
Luo, H., & Wei, Y. (2010). Intuitionistic fuzzy set theory and applications. Springer.
Mellouli, A., & Siala, M. (2013). A new intuitionistic fuzzy correlation coefficient for decision-making problems. International Journal of Fuzzy Systems, 15(3), 513-520. https://doi.org/10.1007/s40815-013-0001-1
Möller, B., & Piskorski, A. (2004). Intuitionistic fuzzy sets and decision-making models. Journal of Intelligent and Fuzzy Systems, 15(1), 53-67. https://doi.org/10.3233/IFS-2004-15106
Rajasekaran, S., & Pai, G. A. (2006). Neural networks, fuzzy logic, and genetic algorithms: Synthesis and applications. Prentice Hall.
Sharma, D., & Gupta, M. (2016). Intuitionistic fuzzy set-based decision-making techniques. International Journal of Mathematical Models and Methods in Applied Sciences, 10(1), 78-84.
Sirdeshmukh, S. A., & Saeed, M. H. (2012). Intuitionistic fuzzy sets and decision-making techniques. International Journal of Advanced Research in Computer Science, 3(4), 532-536.
Sugeno, M. (1985). Industrial applications of fuzzy control. Elsevier Science Inc.
Tan, Y., & Wang, X. (2015). A novel method for ranking intuitionistic fuzzy numbers in decision-making. Computers & Industrial Engineering, 87, 132-141. https://doi.org/10.1016/j.cie.2015.05.006
Yager, R. R., & Filev, D. P. (1994). Essentials of fuzzy modeling and control. Wiley-Interscience.
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