Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application in decision-making

Enginoğlu, Serdar; Arslan, Burak

doi:10.1007/s40314-020-01325-1

Cited by 24 publications

(27 citation statements)

References 38 publications

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“…However, since fpfs-matrices can effectively model classification problems containing uncertainty, further research should be conducted to focus on them. We foresee that one way of improving FPFS-EC is to use different similarity measures of fpfs-matrices or define similarity measures of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices [58]. Another is to employ different soft decisionmaking methods constructed by fpfs-matrices, such as in [9]- [11], [15]- [21], and [59].…”

Section: Discussionmentioning

confidence: 99%

“…The concept of soft sets [1] is a standard and practical mathematical tool used for modeling such uncertainties. Moreover, research on some generalizations of this concept, such as fuzzy soft sets (fs-sets) [2], [3], fuzzy parameterized soft sets [4], fuzzy parameterized fuzzy soft sets [5], soft matrices [6], fuzzy soft matrices [7], fuzzy parameterized fuzzy soft matrices (fpfs-matrices) [8], have been introduced. Due to these generalizations, problems' modeling containing fuzzy parameters and/or fuzzy alternatives (objects) have been possible.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

2021

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In this paper, we first define eight pseudo-metrics and eight pseudo-similarities based on these pseudo-metrics over fpfs-matrices. We then propose a new classification algorithm, i.e. Fuzzy Parameterized Fuzzy Soft Euclidean Classifier (FPFS-EC), based on Euclidean pseudo-similarity. After that, we compare FPFS-EC with Support Vector Machine (SVM), Fuzzy k-nearest neighbor (Fuzzy kNN), Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy kNN Based on the Bonferroni Mean (BM-Fuzzy kNN) in terms of the performance criteria -namely accuracy, precision, recall, macro F-score, and micro F-score -and running time by using 18 real-world datasets in the UCI machine learning repository. The results show that FPFS-EC performs better in the occurrence of the 13 of 18 datasets in question than SVM, Fuzzy kNN, FSSC, FussCyier, HDFSSC, and BM-Fuzzy kNN.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

2021

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“…In this section, firstly, the concept of fpfs-matrices [18,26] and some of its basic definitions have been presented. Throughout this paper, let be a parameter set, ( ) be the set of all fuzzy sets over , and ∈ ( ).…”

Section: Preliminariesmentioning

confidence: 99%

“…To overcome this drawback, several hybrid versions of the soft sets and fuzzy sets [12] have been proposed [13][14][15][16][17]. Among all these versions, fuzzy parameterized fuzzy soft matrices (fpfs-matrices) are the most prominent in terms of performance in the occurrence of decision-making problems in particular [18][19][20][21][22][23][24][25][26]. Therefore, configuring the soft decision-making (SDM) methods in the literature through fpfs-matrices is worth studying.…”

Section: Introductionmentioning

confidence: 99%

Configurations of Several Soft Decision-Making Methods to Operate in Fuzzy Parameterized Fuzzy Soft Matrices Space

Enginoğlu

Öngel

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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The concept of fuzzy parameterized fuzzy soft matrices (fpfs-matrices), which allows for processing fuzzy parameters and fuzzy subsets of the alternatives by using computers, is a novel and efficient mathematical tool to cope with uncertainties. Contrary to the methods constructed by fpfs-matrices, the known soft decision-making (SDM) methods based on soft sets and fuzzy sets cannot model problems whose parameters and alternatives are fuzzy. Therefore, such methods have been configured to operate in the fpfs-matrices space. In this paper, we configure two SDM methods constructed by soft sets, six SDM methods constructed by fuzzy soft sets, two SDM methods constructed by soft matrices, and four SDM methods constructed by fuzzy soft matrices. We then apply the configured methods using one fpfs-matrix as input data to a performance-based value assignment problem. Finally, we discuss the need for further research.

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“…Furthermore, many hybrid versions of this concept, together with soft sets [3], have been introduced, such as intuitionistic fuzzy soft sets [4], intuitionistic fuzzy parameterized soft sets [5], intuitionistic fuzzy parameterized fuzzy soft sets [6], fuzzy parameterized intuitionistic fuzzy soft sets [7], and intuitionistic fuzzy parameterized intuitionistic fuzzy soft sets (ifpifs-sets) [8]. Afterwards, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) [9] modelling all the problems that these concepts can and allowing the data in such problems to be processed in a computer environment has been put forward. Thus, especially for the problems containing a large number of data, it has become possible to process these data on the computer.…”

Section: Introductionmentioning

confidence: 99%

Generalisations of SDM Methods in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Space and Their Application to Performance Ranking of the Noise-Removal Filters

Arslan

Aydın

Memiş³

et al. 2021

Journal of New Theory

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Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) has allowed to mathematically model some problems in which the parameters and alternatives exhibit intuitionistic fuzzy uncertainties. To this end, the present study aims to generalise 24 soft decision-making (SDM) methods operating in the fuzzy parameterized fuzzy soft matrices space with a single fpfs-matrix to the ifpifs-matrices spaces. Afterwards, we propose five test scenarios to analyse whether the SDM methods constructed by ifpifs-matrices consistently work. Moreover, we make performance comparisons of the generalised SDM methods successful in the five test scenarios by applying them to the performance-based value assignment (PVA) problem of the wellknown noise-removal filters. Finally, we discuss the need for further research.

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Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application in decision-making

Cited by 24 publications

References 38 publications

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

Configurations of Several Soft Decision-Making Methods to Operate in Fuzzy Parameterized Fuzzy Soft Matrices Space

Generalisations of SDM Methods in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Space and Their Application to Performance Ranking of the Noise-Removal Filters

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