Survey on rank preservation and rank reversal in data envelopment analysis

Soltanifar, Mehdi; Shahghobadi, Saeid

doi:10.1016/j.knosys.2013.12.021

Cited by 32 publications

(7 citation statements)

References 63 publications

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“…For instance, several studies have debated about the rank reversal problem presented in AHP, a phenomenon consisting of changes observed in the final ranking order after the addition or removal of an alternative (Ishizaka & Labib, ; Rodríguez, Ortega, & Concepción, ; Aires & Ferreira, ). This problem is also a limitation in DEA (Shin, ; Soltanifar & Shahghobadi, ) and Simple Additive Weighting (Aires & Ferreira, ; Bendaoud, Didi, & Abdennebi, ; Shin, Lee, Chun, & Chung, ). Another constraint in the DEA method is that positive outputs and inputs are needed for each decision‐making unit (Huang & Li, ).…”

Section: Primary Studies From the Literaturementioning

confidence: 99%

Section: Modelling Adoption Of Assistive Technologymentioning

confidence: 99%

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Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

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The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.

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Section: Primary Studies From the Literaturementioning

confidence: 99%

Section: Modelling Adoption Of Assistive Technologymentioning

confidence: 99%

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

View full text Add to dashboard Cite

show abstract

“…The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are examples of multi-criteria decision-making (MCDM) methods using in the medical field [2,4,6,8,18,20,21,24]. Unfortunately, the rank reversal phenomenon is still the main shortcoming of MCDM methods [7,41,44]. The commonly used rank reversal definition emphasizes that this issue occurs when the rankings for the alternatives are changed with either the addition or removal of an alternative.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of MCDM Methods for Assessing the Severity of Chronic Liver Disease

Piegat

Sałabun

2015

Lecture Notes in Computer Science

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Abstract. The paper presents the Characteristic Objects method as a potential multi-criteria decision-making method for use in medical issues. The proposed approach is compared with TOPSIS and AHP. For this purpose, assessment of the severity of Chronic Liver Disease (CLD) is used. The simulation experiment is presented on the basis of the Model For End-Stage Liver Disease (MELD). The United Network for Organ Sharing (UNOS) and Eurotransplant use MELD for prioritizing allocation of liver transplants. MELD is calculated from creatinine, bilirubin and international normalized ratio of the prothrombin time (INR). The correctness of the selection is examined among randomly selected one million pairs of patients. The result is expressed as a percentage of agreement between the assessed method and MELD selection. The Characteristic Objects method is completely free of the rank reversal phenomenon, obtained by using the set of characteristic objects. In this approach, the assessment of each alternative is obtained on the basis of the distance from characteristic objects and their values. As a result, correctness of the selection obtained by using the Characteristic Objects method is higher than those obtained by TOPSIS or AHP techniques.

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“…Abri et al developed the ranking rule of non-extreme efficient DMUs [12]. Soltanifar and Shahghobadi provided a survey on rank preservation and rank reversal in the DEA [13].…”

Section: Introductionmentioning

confidence: 99%

Full ranking procedure based on best and worst frontiers

Yang

Liang

et al. 2015

J. of Syst. Eng. Electron.

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In the traditional data envelopment analysis (DEA) structure, the efficiency score for one decision making unit (DMU) is calculated by measuring the distance of the evaluated DMU to the best practice frontier. Recent researches have provided the reasonability of considering the worst practice frontier as a supplement to the traditional DEA techniques. The existing researches take only one type of frontier into account, and they cannot compare the evaluated DMU with both the best and the worst performing DMUs. A DEA-based procedure is developed to consider the best and the worst frontiers in the same scenario where the ratio of two distances (RDS) measure is proposed. The principal application of this approach is for ranking, and, as a complement tool, for performance evaluation. The proposed approach can be used in a wide range of applications such as the performance evaluation of employees and others. Finally, a bookstore data set is used to illustrate the proposed approach.

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Survey on rank preservation and rank reversal in data envelopment analysis

Cited by 32 publications

References 63 publications

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Comparative Analysis of MCDM Methods for Assessing the Severity of Chronic Liver Disease

Full ranking procedure based on best and worst frontiers

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