Fuzzy decision making in complex frameworks with generalized aggregation operators

In the modern world, the computation of vague data is a challenging job. Different theories are presented to deal with such situations. Amongst them, fuzzy set theory and its extensions produced remarkable results. Samrandache extended the theory to a new horizon with the neutrosophic set (NS), which was further extended to interval neutrosophic set (INS). Neutrosophic cubic set (NCS) is the generalized version of NS and INS. This characteristic makes it an exceptional choice to deal with vague and imprecise data. Aggregation operators are key features of decision-making theory. In recent times several aggregation operators were defined in NCS. The intent of this paper is to generalize these aggregation operators by presenting neutrosophic cubic generalized unified aggregation (NCGUA) and neutrosophic cubic quasi-generalized unified aggregation (NCQGUA) operators. The accuracy and precision are a vital tool to minimize the potential threat in decision making. Generally, in decision making methods, alternatives and criteria are considered to evaluate the better outcome. However, sometimes the decision making environment has more components to express the problem completely. These components are named as the state of nature corresponding to each criterion. This complex frame of work is dealt with by presenting the multi-expert decision-making method (MEDMM).

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“…Jose et al [39] analyze the data on fuzzy sets and conclude with the following results. Tables 14 and 15.…”

Section: Discussionmentioning

confidence: 96%

The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method

et al. 2020

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“…The concept of fuzzy generalized unified aggregation operator (FGUAO) was presented by Merigo, Gil-Lafuente, Yu, and Llopis-Albert (2018). The idea of this solution bases on joining within one operator many different averaging operators.…”

Section: Fuzzy Generalized Unified Aggregation Operatormentioning

confidence: 99%

Fuzzy trees and forests—Review

Sosnowski

Gadomer

2019

WIREs Data Min & Knowl

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Data classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show the way they deal with data classification and regression problems. This paper is divided into four parts. In the first part we present the issue of fuzzy clustering, which is one of the most important aspects of fuzzy trees which base on clusters. Some methods of splitting objects into clusters using fuzzy logic are described there. The second part describes different fuzzy decision trees. The way these trees can deal with classification and regression problems is presented. In the third part the issue of forests—ensemble classifiers which consist of fuzzy trees—is described. The last part treats about the way of performing weighted decision making in fuzzy forests. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Classification Technologies > Prediction Technologies > Machine Learning

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“…The OWA operator (Yager, 1988) presents a useful tool to infuse information that reflects the characteristic attitudes of decision makers. Due to its flexibility, the OWA operator has been the focus of many theoretical studies and is used in empirical applications of MADM problems (Merig o, Gil-Lafuente, Yu, & Llopis-Albert, 2018;Tan, Jia, & Chen, 2017;Wei, Gao, & Wei, 2018;Wei & Wang, 2017;Yager, Kacprzyk, & Beliakov, 2011;Yu, 2016;Zhou, Merig o, Chen, & Liu, 2016). Specifically, the OWA operator aggregates the ordered arguments in the following manner:…”

Section: The Owa Operatormentioning

confidence: 99%

“…According to methods mentioned in the recent literature (Merig o et al, 2018;Zeng et al, 2016, we can derive more of the ILOWAWAD operator's special cases, such as the window-ILOWAWAD, the centred-ILOWAWAD and the Olympic-ILOWAWAD.…”

Section: The Ilowawad Operatormentioning

confidence: 99%

Intuitionistic linguistic multi-attribute decision making algorithm based on integrated distance measure

Liu

Wang

et al. 2019

Economic Research-Ekonomska Istraživanja

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This study aims to integrate the intuitionistic linguistic multi-attribute decision making (MADM) method which builds upon an integrated distance measure into supplier evaluation and selection problems. More specifically, an intuitionistic linguistic integrated distance measure based on ordered weighted averaging operator (OWA) and weighted average approach is presented and applied. The desirable characteristics and families of the developed distance operator are further explored. In addition, based on the proposed distance measure, a supplier selection problem for an automobile factory is used to test the practicality of its framework. The effectiveness and applicability of the presented framework for supplier selection are examined by carrying comparative analysis against the existing techniques of aggregation. ARTICLE HISTORY

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Fuzzy decision making in complex frameworks with generalized aggregation operators

Abstract: Research highlights • A new approach for fuzzy decision making with complex environments. • The fuzzy generalized unified aggregation operator. • A wide range of new fuzzy aggregation operators. • An application in a fuzzy economic multi-person decision making.

Cited by 24 publications

References 45 publications

The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method

The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method

Fuzzy trees and forests—Review

Intuitionistic linguistic multi-attribute decision making algorithm based on integrated distance measure

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