Abstract
The correlation coefficient between the two parameters plays a significant part in statistics. Furthermore, the exactness of the assessment of correlation depends upon information from the set of discourses. The data collected for various statistical studies are full of ambiguities. The idea of interval-valued intuitionistic fuzzy soft sets is an extension of intuitionistic fuzzy soft sets that is used to express insufficient evaluation, uncertainty, and anxiety in decision-making. Intuitionistic fuzzy soft sets consider two different types of information, such as membership degree and nonmembership degree. In this paper, the concepts and properties of the correlation coefficient and the weighted correlation coefficient of interval-valued intuitionistic fuzzy soft sets are proposed. A prioritization technique for order preference by similarity to the ideal solution based on interval-valued intuitionistic fuzzy soft sets of correlation coefficients and the weighted correlation coefficient is introduced. We also proposed interval-valued intuitionistic fuzzy soft weighted average and interval-valued intuitionistic fuzzy soft weighted geometric operators and developed decision-making techniques based on the proposed operators. By using the developed techniques, a method for solving decision-making problems is proposed. To ensure the applicability of the proposed methods, an illustrative example is given. Finally, we present a comparison of some existing methods with our proposed techniques.
Highlights
Correlation performs a vital part in statistics and engineering; through correlation analysis, the joint relationship of two variables can be used to evaluate the interdependence of two variables
Garg and Arora [30] developed a generalized version of the intuitionistic fuzzy soft set (IFSS) with weighted averaging and geometric aggregation operators and constructed a decision-making technique to solve problems under an intuitionistic fuzzy environment. ey extended the Maclaurin symmetric mean (MSM) operators to intuitionistic fuzzy soft sets (IFSSs) based on Archimedean T-conorm and T-norm [31]. e idea of entropy measure and TOPSIS based on the correlation coefficient (CC) has been developed by using complex Q-rung orthopair fuzzy information and used the established techniques for decision-making [32]
0.42839 0.51499 0.46154 0.98198 methodologies of IFSSs have some limitations on membership and nonmembership grades, and they cannot deal with parameterizations. e proposed algorithms of interval-valued fuzzy soft set (IVFSS) enhance the existing methodologies, and the decision maker can choose the values from the interval with membership and nonmembership as its limitation. ere is a strong relationship between the proposed model and multiattribute decision-making (MADM) problems
Summary
Correlation performs a vital part in statistics and engineering; through correlation analysis, the joint relationship of two variables can be used to evaluate the interdependence of two variables. Atanassov and Gargov [13] extended the IFS theory and established a new notion which is known as interval-valued intuitionistic fuzzy sets (IFSs). Garg and Arora [30] developed a generalized version of the intuitionistic fuzzy soft set (IFSS) with weighted averaging and geometric aggregation operators and constructed a decision-making technique to solve problems under an intuitionistic fuzzy environment. E idea of entropy measure and TOPSIS based on the correlation coefficient (CC) has been developed by using complex Q-rung orthopair fuzzy information and used the established techniques for decision-making [32]. Our main objective is to introduce a new CC under IVIFSS information and develop the TOPSIS method for IVIFSS based on the proposed CC, intervalvalued intuitionistic fuzzy soft weighted average (IVIFSWA), and interval-valued intuitionistic fuzzy soft weighted geometric (IVIFSWG) operators.
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