Interval-Valued Intuitionistic Fuzzy Soft Rough Approximation Operators and Their Applications in Decision Making Problem

被引：0

作者：

Mukherjee A. ^{[1
]}

机构：

[1] Department of Mathematics, Tripura University, Suryamaninagar, Agartala

[2] Department of Conservative Dentistry and Endodontics, ITS Dental College, Muradnagar, Delhi-Meerut Road, Ghaziabad

来源：

Annals of Data Science | 2022年 / 9卷 / 03期

关键词：

Fuzzy set; Interval-valued fuzzy rough soft set; Intuitionistic fuzzy rough set; Rough set; Soft set;

D O I：

10.1007/s40745-022-00370-3

中图分类号：

学科分类号：

摘要：

It has been found that fuzzy sets, rough sets and soft sets are closely related concepts. Many complicated problems in economics, engineering, social sciences, medical science and many other fields involve uncertain data. These problems, which one comes in real life, cannot be solved using classical mathematical methods. There are several well-known theories to describe uncertainty, for instance, fuzzy set theory, rough set theory, and other mathematical tools. But all of these theories have their inherit difficulties as pointed out by D. Molodtsov. In 1999, D. Molodtsov introduced the concept of soft sets, which can be seen as a new mathematical tool for dealing with uncertainties. The concept of rough sets, proposed by Z. Pawlak as a framework for the construction of approximations of concepts. It is a formal tool for modeling and processing insufficient and incomplete information. Zhou and Wu first proposed the concept of intuitionistic fuzzy rough sets (IFrough sets). The aim of this paper is to introduce the concept of interval-valued intuitionistic fuzzy soft rough sets (IVIFS rough sets). We also investigate some properties of IVIFS rough approximation operators. Some basic operations and properties are studied. Lastly applications have been shown in decision making problems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：611 / 625

页数：14

共 32 条

[1]

Zhou L., Wu W.Z., Characterization of rough set approximati in Atanassov intuitionistic fuzzy set theory, Comput Math Appl, 62, 1, pp. 282-296, (2011)

[2]

Jiang Y., Tang Y., Chen Q., Liu H., Tang J., Interval-valued intuitionistic fuzzy soft sets and their properties, Comput Math Appl, 60, pp. 906-918, (2010)

[3]

Feng F., Liu X., Leoreanu-Fotea V., Jun Y.B., Soft sets and soft rough sets, Inf Sci, 181, 6, pp. 1125-1137, (2011)

[4]

Kumar S., Monitoring novel corona virus (COVID-19) infections in India by cluster analysis, Ann Data Sci, 7, pp. 417-425, (2020)

[5]

Li J., Guo K., Herrera Viedma E., Lee H., Liu J., Zhong Z., Gomes L., Filip F.G., Fang S.C., Ozdemir M.S., Liu X.H., Lu G., Sh Y., Culture vs policy: more global collaboration to effectively combat COVID-19, Innovation, (2020)

[6]

Liu Y., Gu Z., Xia S., Shi B., Zhou X., Shi Y., Liu J., What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization, EClincialMedicine, 22, pp. 100354-100361, (2020)

[7]

Majumder P., Das S., Das R., Tripathy B.C., Identification of the most significant risk factor of COVID-19 in economy using cosine similarity measure under SVPNS-environment, Neutrosophic Sets Syst, 46, pp. 112-127, (2021)

[8]

Shi Y., Tian Y.J., Kou G., Peng Y., Li J.P., Optimization based data mining: theory and applications, (2011)

[9]

Bera A.K., Jana D.K., Banerjee D., Et al., A two-phase multi-criteria fuzzy group decision making approach for supplier evaluation and order allocation considering multi-objective, multi-product and multi-period, Ann Data Sci, 8, pp. 577-601, (2021)

[10]

Mandal W.A., Bipolar pythagorean fuzzy sets and their application in multi attribute decision making problems, Ann Data Sci, (2021)

← 1 2 3 4 →