A hybrid feature selection method for credit scoring

Van, Sang Ha and Ha, Nam Nguyen and Bao, Hien Nguyen Thi (2017) A hybrid feature selection method for credit scoring. EAI Endorsed Transactions on Context-aware Systems and Applications, 4 (11): e2. ISSN 2409-0026

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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.

Item Type: Article
Uncontrolled Keywords: Credit risk, Credit scoring, Hybrid Feature selection, GBM, RFE, Information Values, and Machine learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 16 Sep 2020 08:44
Last Modified: 16 Sep 2020 08:44
URI: https://eprints.eudl.eu/id/eprint/320

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