Hesitant Fuzzy Linear Regression Model for Decision Making

Sultan, Ayesha; Sałabun, Wojciech; Faizi, Shahzad; Ismail, Muhammad

doi:10.3390/sym13101846

Cited by 11 publications

(6 citation statements)

References 52 publications

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“…Thus, a hesitant fuzzy environment for linear regression model could be specified to account for MCDM problems in a hesitant environment. This could represents an interesting and alternative approach suitable when input-output variables are observed as hesitant fuzzy elements [131].…”

Section: Linear Regressionmentioning

confidence: 99%

Multi Criteria Decision Analysis, Hesitant Fuzzy Methodology towards to Expert Systems for analysing financial markets dynamics

Ferrara,

Ciano,

Nava

et al. 2023

Preprint

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Decisions Support Systems are a mixture of different methods and tools combined by machine learning approach. This study uses the most important machine learning techniques (logistic regression, artificial neural networks, and support vector machines) and the expert-based method (fuzzy analytic hierarchy process and hesitant fuzzy numbers) to study some financial markets dynamics. The objective of the study is to examine the main approaches developed by theory and operational practice for the purposes of conceptual representation, management and quality assessment. Different tools are applied to support decisions makers, such as AHPSort II to model the hierarchical structure, FAHP to determine weights in the construction of the matrix of the pairwise comparison and Hesitant Fuzzy Sets (HFS) to better represent the preferences of the decisions makers.

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Section: Linear Regressionmentioning

confidence: 99%

Multi Criteria Decision Analysis, Hesitant Fuzzy Methodology towards to Expert Systems for analysing financial markets dynamics

Ferrara,

Ciano,

Nava

et al. 2023

Preprint

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“…A hesitant fuzzy linear regression model (HFLRM) is investigated in Sultan et al. ( 2021 ) to account for multicriteria decision-making (MCDM) problems in a hesitant environment. In Kizielewicz et al.…”

Section: Introductionmentioning

confidence: 99%

“…He demonstrated that the topology created by the intuitionistic fuzzy metric (M, N ) coincides with the topology generated by the fuzzy metric M for each intuitionistic fuzzy metric space (X , M, N , * , ). A hesitant fuzzy linear regression model (HFLRM) is investigated in Sultan et al (2021) to account for multicriteria decision-making (MCDM) problems in a hesitant environment. In Kizielewicz et al (2021), a new modified TOPSIS technique for dealing with data uncertainty that is based on TFN similarity metrics is offered.…”

Section: Introductionmentioning

confidence: 99%

Bipolar fuzzy metric spaces with application

Zararsız

Riaz

2022

Comp. Appl. Math.

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The concept of a bipolar metric space is presented in this article. In bipolar fuzzy metric space, the notions of continuous t -norms and bipolar continuous symmetry -conorms employs important role. Requirements in metric space, triangular norms are used to extrapolate with the probability density function of triangle inequality. Dual processes of triangular norms are known as triangular conorms. Continuous t -norms and continuous symmetry -conorms are used to define bipolar metric space in this paper. Besides, a number of topological and functional properties of the bipolar metric space have been studied. Afterwards, Baire Category and Uniform Convergence Theorems for bipolar metric spaces are presented. After that, an application on determining the appropriate type of vaccine in the treatment process of COVID-19 is given using similarity measure between bipolar fuzzy metric spaces. For vaccine selection and supply chain management, an advanced multi-attribute decision-making (MADM) algorithm is being developed. Finally, the validity of the proposed MADM method for selecting the best proper form of vaccine is also established.

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“…The parameters of an intuitionistic FLRM are estimated using an LPM that minimizes the total fuzziness of intuitionistic FLRM, which is associated with the width of intuitionistic fuzzy parameters. Sultan et al [ 11 ] developed a fuzzy regression model employing HFS to solve a decision-making problem, in which IPOP variables are observed as hesitant fuzzy elements.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, fuzzy regression models such as the FLRM [ 43 ] and HFLRM were used for decision-making; however, these models do not give distribution information. The novelty of our proposed model PHFLRM [ 11 ] is that it incorporates distribution information to account for multi-criteria decision-making (MCDM) problems.…”

Section: Introductionmentioning

confidence: 99%

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model

Sultan

Sałabun

Faizi

et al. 2022

Sensors

Self Cite

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A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman’s rank correlation test assesses the statistical significance of two rankings sets.

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Hesitant Fuzzy Linear Regression Model for Decision Making

Cited by 11 publications

References 52 publications

Multi Criteria Decision Analysis, Hesitant Fuzzy Methodology towards to Expert Systems for analysing financial markets dynamics

Multi Criteria Decision Analysis, Hesitant Fuzzy Methodology towards to Expert Systems for analysing financial markets dynamics

Bipolar fuzzy metric spaces with application

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model

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