A hybrid feature selection method for credit scoring
Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring mode...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2017-03-01
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| Series: | EAI Endorsed Transactions on Context-aware Systems and Applications |
| Subjects: | |
| Online Access: | http://eudl.eu/doi/10.4108/eai.6-3-2017.152335 |
| _version_ | 1818483797004386304 |
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| author | Sang Ha Van Nam Nguyen Ha Hien Nguyen Thi Bao |
| author_facet | Sang Ha Van Nam Nguyen Ha Hien Nguyen Thi Bao |
| author_sort | Sang Ha Van |
| collection | DOAJ |
| description | Reliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the applicant’s credit score from the input features. Feature scoring expression are combined by feature important (Gini index) and Information Value. Backward sequential scheme is used for selecting optimal subset of relevant features while the subset is evaluated by GBM classifier. To reduce the running time, we applied parallel GBM classifier to evaluate the proposed subset of features. The experimental results showed that the proposed method obtained a higher predictive accuracy than a baseline method for some certain datasets. It also showed faster speed and better generalization than traditional feature selection methods widely used in credit scoring. |
| first_indexed | 2024-12-10T15:46:52Z |
| format | Article |
| id | doaj.art-6be3baccbdbf48b4b3d366fcbfeccf70 |
| institution | Directory Open Access Journal |
| issn | 2409-0026 |
| language | English |
| last_indexed | 2024-12-10T15:46:52Z |
| publishDate | 2017-03-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Context-aware Systems and Applications |
| spelling | doaj.art-6be3baccbdbf48b4b3d366fcbfeccf702022-12-22T01:42:56ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Context-aware Systems and Applications2409-00262017-03-014111610.4108/eai.6-3-2017.152335A hybrid feature selection method for credit scoringSang Ha Van0Nam Nguyen Ha1Hien Nguyen Thi Bao2Department of Economic Information System, Academy of Finance, Hanoi, Viet Nam; sanghv@hvtc.edu.vnDepartment of Information Technology, VNU-University of Engineering and Technology, Hanoi, Viet NamDepartment of Corporate Finance, Academy of Finance, Hanoi, Viet NamReliable credit scoring models played a very important role of retail banks to evaluate credit applications and it has been widely studied. The main objective of this paper is to build a hybrid credit scoring model using feature selection approach. In this study, we constructed a credit scoring model based on parallel GBM (Gradient Boosted Model), filter and wrapper approaches to evaluate the applicant’s credit score from the input features. Feature scoring expression are combined by feature important (Gini index) and Information Value. Backward sequential scheme is used for selecting optimal subset of relevant features while the subset is evaluated by GBM classifier. To reduce the running time, we applied parallel GBM classifier to evaluate the proposed subset of features. The experimental results showed that the proposed method obtained a higher predictive accuracy than a baseline method for some certain datasets. It also showed faster speed and better generalization than traditional feature selection methods widely used in credit scoring.http://eudl.eu/doi/10.4108/eai.6-3-2017.152335Credit riskCredit scoringHybrid Feature selectionGBMRFEInformation Valuesand Machine learning |
| spellingShingle | Sang Ha Van Nam Nguyen Ha Hien Nguyen Thi Bao A hybrid feature selection method for credit scoring EAI Endorsed Transactions on Context-aware Systems and Applications Credit risk Credit scoring Hybrid Feature selection GBM RFE Information Values and Machine learning |
| title | A hybrid feature selection method for credit scoring |
| title_full | A hybrid feature selection method for credit scoring |
| title_fullStr | A hybrid feature selection method for credit scoring |
| title_full_unstemmed | A hybrid feature selection method for credit scoring |
| title_short | A hybrid feature selection method for credit scoring |
| title_sort | hybrid feature selection method for credit scoring |
| topic | Credit risk Credit scoring Hybrid Feature selection GBM RFE Information Values and Machine learning |
| url | http://eudl.eu/doi/10.4108/eai.6-3-2017.152335 |
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