View selection in multi-view stacking: choosing the meta-learner
Advances in Data Analysis and Classification, vol. 19, pp. 579–617
Abstract
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s11634-024-00587-5.
Authors 4
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Affiliation as printed
Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands
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Affiliation as printed
Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands
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Bocconi University · Vrije Universiteit Amsterdam
Affiliation as printed
Department of Mathematics, VU Amsterdam, Amsterdam, The Netherlands
Present Address: Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy
Present Address: Department of Decision Sciences, Bocconi University, Milan, Italy
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Affiliation as printed
Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands
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