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.
@article{arxiv.2310.05566,
title = {Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning},
author = {Mathieu Vu and Emilie Chouzenoux and Ismail Ben Ayed and Jean-Christophe Pesquet},
journal= {arXiv preprint arXiv:2310.05566},
year = {2024}
}
Comments
27 pages, 3 figures, submitted to Signal Processing