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This study first employed the XGBoost model as the foundational framework to construct a more accurate risk matrix. Subsequently, the Bagging ensemble learning method was applied to randomly select multiple subsamples (with replacement) from the original dataset in order to train several base learners. Finally, logistic regression was used for risk warning training, further extracting key risk factors and developing a more precise risk warning model. The experimental results demonstrated that as the number of iterations increased, the F1-scores of each model fluctuated to varying degrees. Nevertheless, the integrated machine learning model proposed in this study consistently maintained a high F1-score across different iteration counts and exhibited stable performance after multiple iterations, highlighting its strong generalization ability and robustness. This study not only validated the effectiveness of ensemble machine learning models in audit risk assessment but also revealed performance differences among various models and explored the underlying reasons through comparative analysis.<\/jats:p>","DOI":"10.1142\/s0218126625503451","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T03:22:40Z","timestamp":1745983360000},"source":"Crossref","is-referenced-by-count":0,"title":["An Ensemble Machine Learning-Based Assessment Model for Accounting Risks"],"prefix":"10.1142","volume":"35","author":[{"given":"Zhi","family":"Liu","sequence":"first","affiliation":[{"name":"Hunan University, Changsha, Hunan 410082, China"},{"name":"Hunan Vocational College of Commerce, Changsha, Hunan 410205, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0301-6362","authenticated-orcid":false,"given":"Ronghui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hunan Vocational College of Commerce, Changsha, Hunan 410205, China"}]},{"given":"Yuwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Hunan Vocational College of Commerce, Changsha, Hunan 410205, China"}]}],"member":"219","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"S0218126625503451BIB001","doi-asserted-by":"publisher","DOI":"10.3390\/buildings14061561"},{"key":"S0218126625503451BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2022.103971"},{"key":"S0218126625503451BIB003","doi-asserted-by":"publisher","DOI":"10.1002\/for.2294"},{"key":"S0218126625503451BIB004","doi-asserted-by":"publisher","DOI":"10.4236\/jfrm.2024.134030"},{"key":"S0218126625503451BIB005","doi-asserted-by":"publisher","DOI":"10.1080\/07366981.2024.2376793"},{"issue":"9","key":"S0218126625503451BIB006","first-page":"79","volume":"20","author":"Kang H.","year":"2024","journal-title":"J. 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