Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring | ||
| Industrial Management Journal | ||
| مقاله 2، دوره 9، شماره 2، 2017، صفحه 245-264 اصل مقاله (350.76 K) | ||
| نوع مقاله: Original Research Article | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2017.226860.1007191 | ||
| نویسندگان | ||
| Ali Eghbali1؛ Seyed Hossein Razavi Hajiagha2؛ Hannan Amoozad* 3 | ||
| 1M.A. in Industrial Management, Khatam University, Tehran, Iran | ||
| 2Assistant Prof. of Management, Khatam University, Tehran, Iran | ||
| 3Assistant Prof. of Industrial Management, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Credit scoring؛ Data Envelopment Analysis؛ Evaluation methods؛ Fitness function؛ Genetic Algorithm؛ Logistic regression؛ Risk Management | ||
| مراجع | ||
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