A new FlowSort-based method to deal with information imperfections in sorting decision-making problems

Pelissari, Renata; Oliveira, Maria Célia de; Amor, Sarah Ben; Abackerli, Alvaro José

doi:10.1016/j.ejor.2019.01.006

Cited by 56 publications

(24 citation statements)

References 30 publications

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“…C . The Fuzzy-FlowSort method (Campos et al 2015) that is part of the overall SMAA-FFS approach (Pelissari et al 2019a) we use in this study is skipped, as we do not intend to use any linguistic variables; hence, to conserve space, we refer the reader interested in this method to the original studies, describing in detail how to use it and/or how to embed it within a SMAA framework respectively.…”

Section: The Flowsort Methodsmentioning

confidence: 99%

“…4. Turning to the fusion of SMAA and FlowSort (Pelissari et al 2019a), the reference profiles could also be simulated within e.g. a given interval.…”

Section: Smaamentioning

confidence: 99%

“…Taking into account the whole spaces W, Q, P and Ψ, in order to define the category acceptability index, a categorisation function h = K(i, q, p, , w) can be defined, showing the category h in which a bank a i is assigned. Following from Pelissari et al (2019a), the category membership function m h i can be defined as:…”

Section: Smaamentioning

confidence: 99%

“…This study complements the latter modelling framework by employing the recently introduced SMAA-Fuzzy-FlowSort (hereafter referred to as 'SMAA-FFS'; Pelissari et al 2019a), the first Stochastic Multiobjective Acceptability Analysis (SMAA) (Lahdelma et al 1998;Lahdelma and Salminen 2001) variant of PROMETHEE-based sorting 1 methods. The framework of SMAA is used as a means to deal with imperfections and uncertainty in real world applications, and it has a wide gamut of applicability that extends and crosses several disciplines (see Pelissari et al 2019b for a recent survey).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A multicriteria decision support tool for modelling bank credit ratings

2020

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We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank's judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

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Section: The Flowsort Methodsmentioning

confidence: 99%

“…4. Turning to the fusion of SMAA and FlowSort (Pelissari et al 2019a), the reference profiles could also be simulated within e.g. a given interval.…”

Section: Smaamentioning

confidence: 99%

Section: Smaamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A multicriteria decision support tool for modelling bank credit ratings

2020

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“…The practical significance of sorting problems has motivated researchers to develop multiple streams of methods for addressing such problems, including (a) value-driven methods (e.g., [10,15,17,18]), (b) outranking-based methods (e.g., [1,5,7,27,31]), and (c) rule induction-oriented models (e.g., [14,20]). In this paper, we focus on the value-driven sorting procedure, which employs a value function as the preference model and assigns a numerical score to each alternative by aggregating its performances on different criteria.…”

Section: Introductionmentioning

confidence: 99%

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

Liu

Kadziński

Liao

et al. 2020

European Journal of Operational Research

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We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows to consider valued assignment examples in which a reference alternative can be classified into multiple classes with respective credibility degrees. We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives. To improve the predictive ability of the constructed model on new instances, we employ the regularization techniques. Moreover, to enhance the capability of addressing large-scale datasets, we introduce a state-of-the-art algorithm that is widely used in the machine learning community to solve the proposed optimization model in a computationally efficient way. Using the constructed additive value model, we determine both crisp and valued assignments for non-reference alternatives. Moreover, we allow the Decision Maker to prioritize importance of classes and give the method a flexibility to adjust classification performance across classes according to the specified priorities. The practical usefulness of the analytical framework is demonstrated on a real-world dataset by comparing it to several existing sorting methods.

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