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To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.<\/jats:p>","DOI":"10.3233\/jifs-211467","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:10:36Z","timestamp":1638886236000},"page":"2127-2142","source":"Crossref","is-referenced-by-count":0,"title":["A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation"],"prefix":"10.1177","volume":"42","author":[{"given":"Fang","family":"He","sequence":"first","affiliation":[{"name":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijia","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information Technology, Zhejiang Yuyin College of Vocational Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211467_ref1","doi-asserted-by":"crossref","first-page":"19864","DOI":"10.1109\/ACCESS.2020.3048018","article-title":"Tax default prediction using feature transformation-based machine learning","volume":"9","author":"Abedin","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211467_ref2","unstructured":"Agarwal A. , Beygelzimer A. , Dud\u00edk M. , Langford J. and Wallach H.M. , A reductions approach to fair classification. 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