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.

翻译：集成学习通过在同一机器学习任务中利用多个模型（即弱学习器）来提升预测性能。基础集成方法对弱学习器的输出进行平均，而更复杂的策略则在弱学习器输出与最终预测之间嵌入机器学习模型。本研究融合了上述两种框架，提出了一种聚合f平均（AFA）浅层神经网络，该网络通过建模并组合不同类型的平均值，实现对弱学习器预测的最优聚合。我们强调其可解释的架构与简洁的训练策略，并通过小样本类增量学习问题验证了其优异性能。