Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision Problems
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Otros documentos de la autoría: García, Vicente; Sánchez Garreta, Josep Salvador; Marqués Marzal, Ana Isabel
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Título
Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision ProblemsFecha de publicación
2019-11-22ISSN
2076-3417; 2076-3417Cita bibliográfica
García, V.; Sánchez, J.S.; Marqués, A.I. Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision Problems. Appl. Sci. 2019, 9, 5052Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2076-3417/9/23/5052/htmVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Although various algorithms have widely been studied for bankruptcy and credit risk prediction, conclusions regarding the best performing method are divergent when using different performance assessment metrics. As a ... [+]
Although various algorithms have widely been studied for bankruptcy and credit risk prediction, conclusions regarding the best performing method are divergent when using different performance assessment metrics. As a solution to this problem, the present paper suggests the employment of two well-known multiple-criteria decision-making (MCDM) techniques by integrating their preference scores, which can constitute a valuable tool for decision-makers and analysts to choose the prediction model(s) more properly. Thus, selection of the most suitable algorithm will be designed as an MCDM problem that consists of a finite number of performance metrics (criteria) and a finite number of classifiers (alternatives). An experimental study will be performed to provide a more comprehensive assessment regarding the behavior of ten classifiers over credit data evaluated with seven different measures, whereas the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) techniques will be applied to rank the classifiers. The results demonstrate that evaluating the performance with a unique measure may lead to wrong conclusions, while the MCDM methods may give rise to a more consistent analysis. Furthermore, the use of MCDM methods allows the analysts to weight the significance of each performance metric based on the intrinsic characteristics of a given credit granting decision problem. [-]
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Applied Sciences, 2019, vol. 9, no 23Derechos de acceso
info:eu-repo/semantics/openAccess
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