Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring
a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance...
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University of Tehran
2017-07-01
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doaj-1662aeee73b04bf08bd6b7abd7580fd72020-11-25T01:17:09ZfasUniversity of Tehranمدیریت صنعتی2008-58852423-53692017-07-019224526410.22059/imj.2017.226860.100719164582Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit ScoringAli Eghbali0Seyed Hossein Razavi Hajiagha1Hannan Amoozad2M.A. in Industrial Management, Khatam University, Tehran, IranAssistant Prof. of Management, Khatam University, Tehran, IranAssistant Prof. of Industrial Management, University of Tehran, Tehran, Irana lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria.https://imj.ut.ac.ir/article_64582_043c9ea09aa272c255dec186a399f0e6.pdfCredit scoringData Envelopment AnalysisEvaluation methodsFitness functionGenetic algorithmLogistic regressionRisk Management |
collection |
DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Ali Eghbali Seyed Hossein Razavi Hajiagha Hannan Amoozad |
spellingShingle |
Ali Eghbali Seyed Hossein Razavi Hajiagha Hannan Amoozad Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring مدیریت صنعتی Credit scoring Data Envelopment Analysis Evaluation methods Fitness function Genetic algorithm Logistic regression Risk Management |
author_facet |
Ali Eghbali Seyed Hossein Razavi Hajiagha Hannan Amoozad |
author_sort |
Ali Eghbali |
title |
Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring |
title_short |
Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring |
title_full |
Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring |
title_fullStr |
Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring |
title_full_unstemmed |
Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring |
title_sort |
performance comparison of genetic algorithm fitness function in customer credit scoring |
publisher |
University of Tehran |
series |
مدیریت صنعتی |
issn |
2008-5885 2423-5369 |
publishDate |
2017-07-01 |
description |
a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria. |
topic |
Credit scoring Data Envelopment Analysis Evaluation methods Fitness function Genetic algorithm Logistic regression Risk Management |
url |
https://imj.ut.ac.ir/article_64582_043c9ea09aa272c255dec186a399f0e6.pdf |
work_keys_str_mv |
AT alieghbali performancecomparisonofgeneticalgorithmfitnessfunctionincustomercreditscoring AT seyedhosseinrazavihajiagha performancecomparisonofgeneticalgorithmfitnessfunctionincustomercreditscoring AT hannanamoozad performancecomparisonofgeneticalgorithmfitnessfunctionincustomercreditscoring |
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1725147842306113536 |