Ranking generalized fuzzy numbers based on centroid and rank index

Xuan, Ha Thi; Yu, Vincent F.

doi:10.1016/j.asoc.2018.03.050

Cited by 47 publications

(26 citation statements)

References 50 publications

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“…Critic 1 Critic 2 Critic 3 Critic 4 Critic 5 + = { [1,1], [5,6], [2,3], [6,6], [1,4], [7,7], [8,9], [1.5, 6.5], [8,8], [10,10] [6,7], [1,3], [6,10], [2,3], [8, 9.2], [9,10], [3,10], [8,8], [10,10]} 0.8635 1.0000 0.9344 0.9706 0.8802 -= { [1,1], [10,10], [4,7], [8,10], [7,8], [9,10], [9.5, 9.5], [1,10], [8,8...…”

Section: Critic Iaa Fuzzy Number Similarity Measurementioning

confidence: 99%

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Similarity measure for aggregated fuzzy numbers from interval-valued data

Gunn

Khorshidi

Aickelin

2020

Soft Computing Letters

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This paper presents a method to compute the degree of similarity between two aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers. The attributes completely redefined or modified within this study include area, perimeter, centroids, quartiles and the agreement ratio. The recommended weighting for each feature has been learned using Principal Component Analysis (PCA). Furthermore, an illustrative example is provided to detail the application and potential future use of the similarity measure.

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Section: Critic Iaa Fuzzy Number Similarity Measurementioning

confidence: 99%

“…! = { [1,1], [1,1], [1,1], [1,1], [1,1]} A " = { [5,6], [6,7], [10,10], [3,4], [5,5]} B # = { [2,3], [1,3], [4,7], [1,3], [4,5] [6,6], [6,10], [8,10], [5,9], [2,3]} D % = { [1,4], [2,3], [7,8], [3,3], [4,4]} E & = { [7,7], [8,9.2], [9,…”

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confidence: 99%

Similarity measure for aggregated fuzzy numbers from interval-valued data

Gunn

Khorshidi

Aickelin

2020

Soft Computing Letters

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“…The Fuzzy set approach in AHP has the purpose of solving the problem of obscurities of human thought that was first used by Zadeh [37,38]. Fuzzy numbers make it possible to solve the problems where criteria are not precisely defined [39]. To solution this, a number of special Triangular Fuzzy Numbers (TFN) were formed into AHP values which are divided into three parts, namely l = lowest value, m = middle value and u = highest value, using (9) [40,41]:…”

Section: Figure 3 Hierarchy Of Purpose Selection Best Algorithmmentioning

confidence: 99%

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Jupri

Sarno

2020

IJEECS

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The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

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“…On top of that, we will need a method of comparing fuzzy numbers. This issue is rather complex and has been treated in a vast number of literature positions (e.g., [40,46,47]), as comparing fuzzy numbers is not unequivocal. Here, we propose to use one of many possible ways of defining the distance and the similarity degree between two triangular fuzzy numbers [48].…”

Section: Definitionmentioning

confidence: 99%

Application of Fuzzy Sets to the Expert Estimation of Scrum-Based Projects

Rola

Kuchta

2019

Symmetry

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The paper is basically dedicated to the problem of effort estimation for the Product Backlog items of IT projects led accordingly to the Scrum framework. The effort estimation issue is important, because low quality estimation decreases the efficiency of project implementation. The paper proposes an estimation method for the Product Backlog items of Scrum-based IT projects (which can be adapted also to other projects), which has two original elements with respect to the state of art in Scrum estimation: the usage of fuzzy numbers and strict rules for consensus forming, combined with a space for human interaction. The assumptions of the method should be complied with and were formulated on the basis of literature and authors experience. Two case studies were used for an initial method validation. The case studies confirmed a high potential of the method to increase estimation quality in Scrum-based projects, as well as in other project types. The case studies were conducted using research methods fulfilling the symmetry principle. The paper is thus an example of symmetry in management research.

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Ranking generalized fuzzy numbers based on centroid and rank index

Cited by 47 publications

References 50 publications

Similarity measure for aggregated fuzzy numbers from interval-valued data

Similarity measure for aggregated fuzzy numbers from interval-valued data

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Application of Fuzzy Sets to the Expert Estimation of Scrum-Based Projects

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