We know that stacking is the most popular meta-learning technique. It learns from the predictions of the base learners that learn from the training dataset. Now assuming there are two base learners, denoted as A and B, they have been trained on the training dataset training dataset $D_{train}$. Learner A outperform distinctly B on the validation dataset $D_{validation}$, as indicated by the ROC curve totally enclosing that of B on $D_{validation}$, as illustrated by the following figure. Under such case, can stacking of A and B further improve the performance on $D_{validation}$?
I encountered this question when dealing with my data in geosciences, which indicated the stacked meta-learner can only achieve as most good result as that of A. Since the data is in private, I cannot share the project.
This figure is just for illustration, which is cited from:https://classeval.wordpress.com/introduction/introduction-to-the-roc-receiver-operating-characteristics-plot/