Although various algorithms have widely been studied for bankruptcy and credit risk\nprediction, conclusions regarding the best performing method are divergent when using different\nperformance assessment metrics. As a solution to this problem, the present paper suggests the employment\nof two well-known multiple-criteria decision-making (MCDM) techniques by integrating their preference\nscores, which can constitute a valuable tool for decision-makers and analysts to choose the prediction\nmodel(s) more properly. Thus, selection of the most suitable algorithm will be designed as an MCDM\nproblem that consists of a finite number of performance metrics (criteria) and a finite number of classifiers\n(alternatives). An experimental study will be performed to provide a more comprehensive assessment\nregarding the behavior of ten classifiers over credit data evaluated with seven different measures, whereas\nthe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking\nOrganization METHod for Enrichment of Evaluations (PROMETHEE) techniques will be applied to rank\nthe classifiers. The results demonstrate that evaluating the performance with a unique measure may lead\nto wrong conclusions, while theMCDMmethods may give rise to a more consistent analysis. Furthermore,\nthe use of MCDM methods allows the analysts to weight the significance of each performance metric\nbased on the intrinsic characteristics of a given credit granting decision problem.
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