{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:31:13Z","timestamp":1777696273642,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,11,12]]},"abstract":"<jats:p>Establishing precise credit scoring models to predict the potential default probability is vital for credit risk management. Machine learning models, especially ensemble learning approaches, have shown substantial progress in the performance improvement of credit scoring. The Bagging ensemble approach improves the credit scoring performance by optimizing the prediction variance while boosting ensemble algorithms reduce the prediction error by controlling the prediction bias. In this study, we propose a hybrid ensemble method that combines the advantages of the Bagging ensemble strategy and boosting ensemble optimization pattern, which can well balance the tradeoff of variance-bias optimization. The proposed method considers XGBoost as a base learner, which ensures the low-bias prediction. Moreover, the Bagging strategy is introduced to train the base learner to prevent over-fitting in the proposed method. Besides, the Bagging-boosting ensemble algorithm is further assembled in a cascading way, making the proposed new hybrid ensemble algorithm a good solution to balance the tradeoff of variance bias for credit scoring. Experimental results on the Australian, German, Japanese, and Taiwan datasets show the proposed Bagging-cascading boosted decision tree provides a more accurate credit scoring result.<\/jats:p>","DOI":"10.3233\/ida-216228","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T11:34:11Z","timestamp":1667561651000},"page":"1557-1578","source":"Crossref","is-referenced-by-count":0,"title":["Credit scoring based on a Bagging-cascading boosted decision tree"],"prefix":"10.1177","volume":"26","author":[{"given":"Yao","family":"Zou","sequence":"first","affiliation":[{"name":"Glorious Sun School of Business and Management, Donghua University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changchun","family":"Gao","sequence":"additional","affiliation":[{"name":"Glorious Sun School of Business and Management, Donghua University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congyuan","family":"Pang","sequence":"additional","affiliation":[{"name":"Glorious Sun School of Business and Management, Donghua University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216228_ref1","doi-asserted-by":"crossref","first-page":"104036","DOI":"10.1016\/j.engappai.2020.104036","article-title":"Step-wise multi-grained augmented gradient boosting decision trees for credit scoring","volume":"97","author":"Liu","year":"2021","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.3233\/IDA-216228_ref2","first-page":"1","article-title":"Multi-grained and multi-layered gradient boosting decision tree for credit scoring","author":"Liu","year":"2021","journal-title":"Applied Intelligence"},{"issue":"3","key":"10.3233\/IDA-216228_ref3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/0378-4266(78)90012-2","article-title":"Problems in applying discriminant analysis in credit scoring models","volume":"2","author":"Eisenbeis","year":"1978","journal-title":"Journal of Banking & Finance"},{"key":"10.3233\/IDA-216228_ref4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.asoc.2016.02.025","article-title":"Technology credit scoring model with fuzzy logistic regression","volume":"43","author":"Sohn","year":"2016","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-216228_ref5","doi-asserted-by":"crossref","unstructured":"A.C. 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