View selection in multi-view stacking: choosing the meta-learner

Wouter Loon (), Marjolein Fokkema, Botond Szabo and Mark Rooij
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Wouter Loon: Leiden University
Marjolein Fokkema: Leiden University
Botond Szabo: VU Amsterdam
Mark Rooij: Leiden University

Advances in Data Analysis and Classification, 2025, vol. 19, issue 3, No 3, 579-617

Abstract: Abstract Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner 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.

Keywords: Multi-view learning; Stacked generalization; Feature selection; Classification; 62; 68 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00587-5

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