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Enterprise credit risk data in the supply chain are characterized by higher\u2010dimension information and class imbalance. The class imbalance influences the feature selection effect, and the feature subset is closely related to the predictive performance of subsequent learning algorithms. Therefore, ensuring the adaptivity of feature selection and the subsequent class imbalance\u2013oriented classification model is a key issue. We propose an AdaBoost ensemble model with fast nondominated feature selection (AdaFNDFS). AdaFNDFS uses the FNDFS method in the AdaBoost algorithm to iteratively select features and uses the classifier to evaluate the performance of feature subsets to train the class imbalance\u2013oriented classifier and the best\u2010matched feature subset, ensuring the adaptivity of feature selection and subsequent classifiers. The further use of the differential sampling rate (DSR) method enables AdaFNDFS to integrate more training models with different knowledge and to obtain higher accuracy and better generalization ability for prediction tasks facing high\u2010dimensional information and class imbalance. A test using credit risk data from Chinese listed enterprises containing supply chain information demonstrates that the prediction scoring indicators, such as AUC, KS, AP, and accuracy, of the AdaFNDFS are better than those of basic models such as LR, LDA, DT, and SVM and multiple hybrid models that use SMOTE, feature selection, and ensemble methods. AdaFNDFS outperforms the basic models by at least 0.0073 (0.0344, 0.0349, and 0.0071) in terms of the AUC (KS, AP, and accuracy). AdaFNDFS has outstanding advantages in predicting enterprise credit risk in the supply chain and can support interested decision\u2010makers.<\/jats:p>","DOI":"10.1155\/2024\/5529847","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T13:10:47Z","timestamp":1730207447000},"source":"Crossref","is-referenced-by-count":8,"title":["AdaFNDFS: An AdaBoost Ensemble Model with Fast Nondominated Feature Selection for Predicting Enterprise Credit Risk in the Supply Chain"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3007-2382","authenticated-orcid":false,"given":"Gang","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojian","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingfan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taiyun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ammar","family":"Yasir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suizhi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2023.102604"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2023.2257807"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1108\/k-12-2021-1376"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-09158-9"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118026"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1111\/irfi.12176"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/0022-1082.00389"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12953"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.07.006"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106462"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12680"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107959"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22230"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108153"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/4196920"},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110088"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/8812844"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117002"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120491"},{"key":"e_1_2_11_20_2","first-page":"855","article-title":"Credit Scoring by Artificial Neural Networks Based Cross-Entropy and Fuzzy Relations","volume":"37","author":"Ilter D.","year":"2019","journal-title":"Sigma Journal of Engineering and Natural Sciences"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.06.068"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116202"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1059-8"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.2614"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2023.02.027"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2022.09.003"},{"key":"e_1_2_11_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115290"},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04911-x"},{"key":"e_1_2_11_30_2","doi-asserted-by":"crossref","unstructured":"Seijo-PardoB. 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