Ensemble learning leverages multiple models (i.e., weak learners) on a common
machine learning task to enhance prediction performance. Basic ensembling
approaches average the weak learners outputs, while more sophisticated ones
stack a machine learning model in between the weak learners outputs and the
final prediction. This work fuses both aforementioned frameworks. We introduce
an aggregated f-average (AFA) shallow neural network which models and combines
different types of averages to perform an optimal aggregation of the weak
learners predictions. We emphasise its interpretable architecture and simple
training strategy, and illustrate its good performance on the problem of
few-shot class incremental learning.Comment: 6 page