{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:44:09Z","timestamp":1771015449024,"version":"3.50.1"},"reference-count":41,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"crossref","award":["2021JJ30742"],"award-info":[{"award-number":["2021JJ30742"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["1B0349"],"award-info":[{"award-number":["1B0349"]}]},{"name":"Hunan Province Science and Technology","award":["2018TP1036"],"award-info":[{"award-number":["2018TP1036"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702053"],"award-info":[{"award-number":["61702053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872135"],"award-info":[{"award-number":["61872135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p> It is crucial to correctly assess whether a potential borrower can repay the loan in the credit scoring model. The credit loan data has a serious data imbalance because the number of defaulters is far less than the nondefaulters. However, most current methods for dealing with data imbalance are designed to improve the classification performance of minority data, which will reduce the performance of majority data. For a financial institution, the economic loss caused by the decrease in the classification performance of nondefaulters (majority data) cannot be ignored. This paper proposes an ensemble learning method based on one-class and binary classification (EMOBC) for credit scoring. The purpose is to improve the classification accuracy of the minority class while mitigating the loss of classification accuracy of the majority class as much as possible. EMOBC uses undersampling for the majority class (nondefault samples in credit scoring) and perform binary-class learning on the balanced data to improve the classification accuracy of the minority. To alleviate the decline in classification performance of the majority class, EMOBC uses one-class and binary collaborative classification to train classifiers. The classification result is determined by the average of one-class and binary-class classifiers. The experimental results show that EMOBC has good comprehensive performance compared with the existing methods. <\/jats:p>","DOI":"10.1142\/s0218126623502560","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T07:04:40Z","timestamp":1678431880000},"source":"Crossref","is-referenced-by-count":1,"title":["An Ensemble Learning Method Based on One-Class and Binary Classification for Credit Scoring"],"prefix":"10.1142","volume":"32","author":[{"given":"Zaimei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Changsha University of Science and Technology, Changsha, Hunan, P. R. 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