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

arXiv:2010.16271v3 Announce Type: replace-cross
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 …

abstract algorithm arxiv cs.lg feature framework information meta objects predictions set stat.me stat.ml study type view