Performance Comparison of Genetic Algorithm Fitness Function in Customer Credit Scoring
| Autor: | Ali Eghbali, Seyed Hossein Razavi Hajiagha, Hannan Amoozad |
|---|---|
| Jazyk: | perština |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | مدیریت صنعتی, Vol 9, Iss 2, Pp 245-264 (2017) |
| Druh dokumentu: | article |
| ISSN: | 2008-5885 2423-5369 |
| DOI: | 10.22059/imj.2017.226860.1007191 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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