An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers

Hejazi, Seyed Reza; Doostparast, A.; Hosseini, Sayyed Mahdi

doi:10.1016/j.eswa.2011.01.101

Cited by 81 publications

(49 citation statements)

References 6 publications

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“…However, from Table 1, we can see that if we apply the methods presented by Chen (1996), Lee (2002) and Hejazi et al (2011), Set 10 and Set 16 get the same degree of similarity, respectively, and if we apply the method presented by Wei & Chen (2009), the result shows that Set 16 is more similar than Set 10. They are not the correct results.…”

Section: ) P ( B)) + Min(a( A) A( B)) Max(p ( A) P ( B)) + Max(a( mentioning

confidence: 91%

“…Many similarity measures between fuzzy numbers have been proposed [15][16][17][18][19]. However, it has been found that the existing methods cannot correctly calculate the degree of similarity between two generalized fuzzy numbers in some situations.…”

Section: An Improved Similarity Measure Of Generalized Fuzzy Numbersmentioning

confidence: 99%

“…In this section, we briefly introduce some existing similarity measures between fuzzy numbers from Chen [15], Lee [16], Chen and Chen [17], Wei & Chen [18] and Hejazi, et al [19].…”

Section: A Review Of the Existing Similarity Measures Between Fuzzy Nmentioning

confidence: 99%

“…[19] presented an improved similarity measure between two fuzzy numbers A and B combining the concept of geometric distance, height, areas and perimeters of generalized fuzzy numbers. The degree of similarity S( A, B) between the generalized trapezoidal fuzzy numbers A and B is calculated as follows:…”

Section: A Review Of the Existing Similarity Measures Between Fuzzy Nmentioning

confidence: 99%

See 3 more Smart Citations

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Jiang¹,

Yan

Luo

et al. 2015

INT J COMPUT COMMUN

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Dempster-Shafer theory of evidence has been widely used in many data fusion application systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. The proposed method can overcome the drawbacks of the existing similarity measures. Then, we propose a new method for obtaining basic probability assignment (BPA) based on the proposed similarity measure method between generalized fuzzy numbers. Finally, the efficiency of the proposed method is illustrated by the classification of Iris data.

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Section: ) P ( B)) + Min(a( A) A( B)) Max(p ( A) P ( B)) + Max(a( mentioning

confidence: 91%

Section: An Improved Similarity Measure Of Generalized Fuzzy Numbersmentioning

confidence: 99%

“…In this section, we briefly introduce some existing similarity measures between fuzzy numbers from Chen [15], Lee [16], Chen and Chen [17], Wei & Chen [18] and Hejazi, et al [19].…”

Section: A Review Of the Existing Similarity Measures Between Fuzzy Nmentioning

confidence: 99%

Section: A Review Of the Existing Similarity Measures Between Fuzzy Nmentioning

confidence: 99%

See 2 more Smart Citations

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Jiang¹,

Yan

Luo

et al. 2015

INT J COMPUT COMMUN

View full text Add to dashboard Cite

show abstract

“…Several authors have proposed different similarity functions, which are based on the centroid of a fuzzy number and the distance between the components of the fuzzy numbers in TF [0,1], [4,15,11,5,6,28,9]. A similarity function was proposed and 30 sets of linguistic value trapezoidal fuzzy numbers were used to compare the calculation results with other previously proposed functions in [7].…”

Section: Similarity Functionmentioning

confidence: 99%

Risk analysis in information systems: A fuzzification of the MAGERIT methodology

Vicente

Mateos

Jiménez-Martín

2014

Knowledge-Based Systems

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Several methodologies based on ISO/IEC 27000 international standard have been developed to deal with risk analysis in information systems (IS). These methodologies do not, however, consider imprecise val-uations, but use precise values on different, usually percentage, scales.We propose an extension of the MAGERIT methodology based on classical fuzzy computational models. A linguistic term scale is used to represent asset values, their dependencies and frequency and asset deg-radation associated with threats. Computations are based on trapezoidal fuzzy numbers associated with linguistic terms. A similarity function is used to associate a linguistic term on the previously defined scale to the trapezoidal fuzzy numbers resulting from computations. Finally, regarding the selection of preven-tive safeguards to reduce risks in IS, we propose a dynamic programming-based method that incorpo-rates simulated annealing to tackle optimizations problems with the aim of minimizing costs while keeping the risk at acceptable levels.An example of an administrative unit using in-house and third-party information systems internally and to provide public information services is used to illustrate the methodology.

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A Fuzzy Model for Selecting Safeguards to Reduce Risks in Information Systems

Vicente

Mateos

Jiménez-Martín

2015

Operations Research and Enterprise Systems

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An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers

Cited by 81 publications

References 6 publications

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Risk analysis in information systems: A fuzzification of the MAGERIT methodology

A Fuzzy Model for Selecting Safeguards to Reduce Risks in Information Systems

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