Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs

Kouatli, Issam

doi:10.1108/03684920810851069

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…According to [29], it is very difficult to reasonably express situations that are complex, or difficult to define, using traditional quantification methods; thus the concept of linguistic variables, “which are variables whose values are words or sentences”, is needed [30–32]. To present this linguistic variable as a number in an interval [0,1], fuzzy sets theory has been frequently utilized.…”

Section: Introductionmentioning

confidence: 99%

Development of interval-valued fuzzy GRA with SERVPERF based on subjective and objective weights for evaluation of airline service quality: A case study of Korea low-cost carriers

Lee

2019

PLoS ONE

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As the airline industry has become ever-more competitive and profitability more tenuous, airline service quality management has grown more important to airlines. Although many studies have focused on the evaluation of airline service quality, some common limitations need to be noted. First, traditional fuzzy logics were utilized to present linguistic variables as fuzzy numbers. However, precise quantification of lower and upper bounds with a single number is often difficult; thus, interval-valued fuzzy sets that represent the lower and upper bounds in the fuzzy number as an interval form should be applied instead. Second, while some studies have applied various multiple-criteria decision-making method [MCDM] and the service quality (SERVQUAL) method for evaluation of airline service quality, few have utilized grey relational analysis (GRA, a simple and data-driven MCDM method applicable to environments with incomplete information) and the service performance (SERVPERF), a performance-based measure that can resolve the ambiguity issue of the expectations construct in SERVQUAL. Third, extant studies dealing with the issue of weighting criteria in the evaluation of airline service quality have focused only on either subjective or objective weights, though weighting criteria based on a combined objective/subjective approach would be much better than those just considering the subjective approach. The present study endeavored to fill these literature gaps by developing, for evaluation of airline service quality, interval-valued fuzzy GRA with SERVPERF based on both subjective and objective weights. It contributes to the field by incorporating the 22 criteria from SERVPERF to effectively account for the various characteristics of airline service. Additionally, it is the first study to utilize interval-valued fuzzy GRA together with a novel technique that combines a subjective/objective weighting method for integration of objective decision-matrix-derived information with subjective decision-maker preferences. The supplemental empirical case study of airline service evaluation, further, provides researchers and practitioners with a means of better understanding the proposed approach in the practical perspectives.

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Section: Introductionmentioning

confidence: 99%

Development of interval-valued fuzzy GRA with SERVPERF based on subjective and objective weights for evaluation of airline service quality: A case study of Korea low-cost carriers

Lee

2019

PLoS ONE

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“…Some automatic approaches for the FDB definition include the use of genetic algorithms [18], [26], the concept of fuzzymetric arcs [22], clonal selection algorithms [2], artificial neural networks [1], entropy [20], fuzzy entropy and fuzziness [25], and also the Kappa measure [13], among others.…”

Section: Automatic Definition Of Fuzzy Data Basesmentioning

confidence: 99%

“…Some of these proposals include the use of a fixed number of fuzzy sets evenly distributed for all attributes, namely the equalized universe method [6], the use of genetic algorithms [26], the concept of fuzzymetric arcs [22], and clonal selection algorithms [2], among others. In order to provide a fast and reliable method to define FDBs, we proposed the FUZZY-DBD method in [10], which combines an attribute fuzzyfication step with an estimation function to define the best number of fuzzy sets for each independent attribute.…”

Section: Introductionmentioning

confidence: 99%

Using fuzzy formal concepts in the genetic generation of fuzzy systems

Cintra

Monard

Camargo

2012

2012 IEEE International Conference on Fuzzy Systems

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Fuzzy classification systems have been widely researched in the literature. Genetic fuzzy systems combine the power of the global search of genetic algorithms with fuzzy systems to provide accurate and interpretable rule-based systems. In this paper, we present a new approach for the genetic generation of fuzzy systems. The novelty of our proposal, named FCA-BASED method, is a hybrid combination of fuzzy formal concepts to extract rules to form the search space of a genetic algorithm. FCA-BASED extracts rules from existing data using the fuzzy formal concept analysis theory. FCA-BASED uses the set of a priori extracted rules to form the final fuzzy rule bases by means of its genetic process. FCA-BASED was tested using 10 datasets and a 10-fold cross-validation strategy using 4 different fuzzy data bases. The main comparisons included in this work are related to the number of extracted rules forming the genetic search spaces between FCA-BASED and DOC-BASED. Results are then analysed according to their accuracy and intepretability. The obtained results are adequate for the tested datasets.

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“…In this case a crossover and mutation operations has to be re-defined in the context of Fuzzimetric systems. A good illustrative decision making example of how to use Fuzzimetric Arcs can be found in Kouatli [3] where a financial example of whether or not to buy bonds using interest rates as input and bond price as the output of the system. GFS tuning algorithm using Fuzzimetric Arcs was also described in Kouatli[4] where more emphasis of Multivariable structure of GFT was also demonstrated in Kouatli [5].…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzimetric Technique (GFT): A new optimization methodology using the concept of Fuzzimetric Arcs

Kouatli

2012

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS

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Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. This integration is usually referred to as Genetic Fuzzy systems (GFS) where different researchers adopted different techniques to achieve the functionality of GFS. This paper proposes another new methodology based on the concept of Fuzzimetric Arcs which is also reviewed in this paper. This new proposed technique is termed as Genetic Fuzzimetric Technique (GFT) where the strength of this technique is based on the systematic approach of defining fuzzy sets (variables), cross-over and mutation of these variables in order to find the optimized performance.Most of real life decision making processes are of that type of uncertainty and hence the need of a fuzzy system that can be optimized. One such problem is to decide on the expected performance level of the student during the admission process to the university. This example was taken as a vehicle to clarify the mechanism of GFT.

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Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs

Cited by 11 publications

References 5 publications

Development of interval-valued fuzzy GRA with SERVPERF based on subjective and objective weights for evaluation of airline service quality: A case study of Korea low-cost carriers

Development of interval-valued fuzzy GRA with SERVPERF based on subjective and objective weights for evaluation of airline service quality: A case study of Korea low-cost carriers

Using fuzzy formal concepts in the genetic generation of fuzzy systems

Genetic Fuzzimetric Technique (GFT): A new optimization methodology using the concept of Fuzzimetric Arcs

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