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In this framework, a <jats:italic>base-learner<\/jats:italic> algorithm is trained on each view separately, and their predictions are then combined by a <jats:italic>meta-learner<\/jats:italic> algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.\n<\/jats:p>","DOI":"10.1007\/s11634-024-00587-5","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T16:17:03Z","timestamp":1712938623000},"page":"579-617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["View selection in multi-view stacking: choosing the meta-learner"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4701-9265","authenticated-orcid":false,"given":"Wouter","family":"van Loon","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-8325","authenticated-orcid":false,"given":"Marjolein","family":"Fokkema","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5526-8747","authenticated-orcid":false,"given":"Botond","family":"Szabo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7308-6210","authenticated-orcid":false,"given":"Mark","family":"de Rooij","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"587_CR1","unstructured":"Anagnostopoulos C, Hand DJ (2019) . hmeasure: the H-measure and other scalar classification performance metrics https:\/\/CRAN.R-project.org\/package=hmeasure R package version 1.0-2"},{"key":"587_CR2","unstructured":"Ballings M, Van den Poel D (2013) AUC: threshold independent performance measures for probabilistic classifiers. https:\/\/CRAN.R-project.org\/package=AUC R package version 0.3.0"},{"issue":"1","key":"587_CR3","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1002\/bimj.200900064","volume":"52","author":"A Benner","year":"2010","unstructured":"Benner A, Zucknick M, Hielscher T, Ittrich C, Mansmann U (2010) High-dimensional cox models: the choice of penalty as part of the model building process. 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