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Eight machine learning models that have good performance and are widely used in other fields, including Radial Basis Function Neural Network, Random Forest, eXtreme Gradient Boosting, and so on, are used as component learners in this study. The hyperparameters of each model are obtained by cross-validation and grid search. The advancedness of the proposed method is verified by comparing it with other models on a small self-built ship stability failure probability dataset. By conducting experiments that simply average the results of the component learners, it is confirmed that simple superposition different models does not necessarily improve the accuracy. At the same time, after pre-processing the input features in different ways, the comparison of the prediction performance was conducted, and the experimental results showed that the proposed method is not affected by the way the input features are preprocessed and therefore has some robustness.<\/jats:p>","DOI":"10.1007\/s40747-024-01363-w","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T08:03:05Z","timestamp":1709020985000},"page":"3873-3890","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A joint multi-model machine learning prediction approach based on confidence for ship stability"],"prefix":"10.1007","volume":"10","author":[{"given":"Chaicheng","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6215-9864","authenticated-orcid":false,"given":"Xianbo","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Gong","family":"Xiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"1363_CR1","unstructured":"IMO SDC 8\/INF.2 (2021) Physical background and mathematical models for stability failures of the second generation intact stability criteria"},{"key":"1363_CR2","doi-asserted-by":"publisher","unstructured":"Witczak M, Pazera M (2019). 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