A New Credit Risk System Using Hybrid ELECTRE TRI and NSGA-II Methods

Journal of System Management

دوره 6، شماره 4، اسفند 2020، صفحه 1-25   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/jsm.2021.1924341.1445

نویسندگان

Amir Modjtahedi^{* 1}؛ Amir Daneshvar²

¹Business School, College of Business, Law and Social Sciences, University of Derby, Derby, UK

²Department of Industrial Management, Electronic Branch, Islamic Azad University, Tehran, Iran

چکیده

ELECTRE TRI is the most applicable and developed outranking based classification method in the field of MCDA. By including a large number of parameters, it provides a huge amount of information on criteria which enriches decision making process, although calculation of these large number of parameters is very time consuming and difficult task. To tackle this problem, this paper proposes a new method called NSGA-ELECTRE, by which the NSGA-  algorithm learns ELECTRE TRI and elicits its parameters through an evolutionary process. The proposed method contributes to the literature by utilizing a pair of conflicting objective functions including Type I errors and Type II errors instead of using a single criterion named “classification accuracy” which used frequently in the related works. ­­­The proposed bi-objective method is applied to six known credit risk datasets. The NRGA model is used as a benchmark for validation. Computational results indicate outstanding performance of the NSGA-ELECTRE method.

کلیدواژه‌ها

ELECTRE TRI؛ NSGA-ΙΙ؛ Credit risk؛ NRGA

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