Simplifying the Search for Effective Ranking of Fuzzy Numbers

Ban, Adrian I.; Coroianu, Lucian

doi:10.1109/tfuzz.2014.2312204

Cited by 37 publications

(21 citation statements)

References 51 publications

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“…There are several methods to convert fuzzy numbers into crisp real numbers. The expected value is between the most common approaches (see Heilpern, 1992;Ban & Coroianu, 2015). For a triangular fuzzy number (l, m, u), the expected value is l m u 2 4…”

Section: Triangular Fuzzy Numbersmentioning

confidence: 99%

Performance Evaluation Model of Romanian Manufacturing Listed Companies by Fuzzy Ahp and Topsis

Ban

Bogdan

et al. 2020

Technological and Economic Development of Economy

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We are interested in the hierarchy of the main Romanian companies in the manufacturing industry by considering eight financial and seven non-financial indicators. Thirty three listed companies, that are non-financial institutions, were selected for the study and in order to control the reliability of the data we used the Bucharest Stock Exchange database, official data published by the Romanian Ministry of Public Finance, and the annual reports released by the companies on their websites, collecting information for the years 2011–2015. Because the human thinking is subjective and ambiguous we prefer linguistic variables, converted afterwards in triangular fuzzy numbers, to represent the importance of indicators. Our method involves the calculation of the weights of individual or categories of indicators based on Fuzzy Analytic Hierarchy Process. Then, the level of performance for each company, separately for financial, non-financial and all indicators is obtained by TOPSIS method. We deduce an objective hierarchy of the companies on a rigorous basis, which is however dependent from the choice of indicators and the conversion scale of linguistic variables into triangular fuzzy numbers. Also, following the obtained results we concluded that the overall performance of companies for the analyzed period is significantly influenced by non-financial indicators.

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Section: Triangular Fuzzy Numbersmentioning

confidence: 99%

Performance Evaluation Model of Romanian Manufacturing Listed Companies by Fuzzy Ahp and Topsis

Ban

Bogdan

et al. 2020

Technological and Economic Development of Economy

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“…The partial fuzzy indifference relation is represented by the trapezoidal membership function. This type of functions are well studied in the references [42] and [9]. Table 2 presents the performance matrix which is used to evaluate the prototype of classes on a set of attributes.…”

Section: Computing the Fuzzy Indifference Relationmentioning

confidence: 99%

Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

Al-Obeidat

Belacel

Spencer

2019

Lecture Notes in Computer Science

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The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems.

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“…In Ban, Coroianu (2015) it was proved that the expected value is a simple and effective ranking index on fuzzy numbers, therefore on triangular fuzzy numbers too. Namely, for two triangular fuzzy numbers D and D¢ we define:…”

Section: ( )mentioning

confidence: 99%

Evaluation of Hotel Quality Attribute Importance Through Fuzzy Correlation Coefficient

Ban

Tara

Bogdan

et al. 2016

Technological and Economic Development of Economy

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An indirect method of calculation of the importance of attributes as the fuzzy correlation between the performance of attributes and the overall satisfaction was proposed in a recent paper. We apply the method to the results of a survey with respect to the quality of hotel services in Oradea (Romania). Different representations of the answers as triangular fuzzy numbers, as well as distinct analyzes to compare the hierarchies of the attributes with respect to the experience with the hotel and the motivation of the travel are considered.

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Simplifying the Search for Effective Ranking of Fuzzy Numbers

Cited by 37 publications

References 51 publications

Performance Evaluation Model of Romanian Manufacturing Listed Companies by Fuzzy Ahp and Topsis

Performance Evaluation Model of Romanian Manufacturing Listed Companies by Fuzzy Ahp and Topsis

Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

Evaluation of Hotel Quality Attribute Importance Through Fuzzy Correlation Coefficient

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