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

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

SUBMITTER: van Loon W 

PROVIDER: S-EPMC12449401 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature