An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification
The selection of a meta-learner determines the success of a stacked ensemble as the meta-learner is responsible for the final predictions of the stacked ensemble.Unfortunately, in imbalanced classification, selecting an appropriate here and well-performing meta-learner of stacked ensemble is not straightforward as different meta-learners are advoca