@inproceedings{
tifrea2023improving,
title={Improving class and group imbalanced classification with uncertainty-based active learning},
author={Alexandru Tifrea and John Hill and Fanny Yang},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=0CPnNCOFiI}
}
Recent experimental and theoretical analyses have revealed that
uncertainty-based active learning algorithms (U-AL) are often not able to
improve the average accuracy compared to even the simple baseline of passive
learning (PL). However, we show in this work that U-AL is a competitive
method in problems with severe data imbalance, when instead of the
\emph{average} accuracy, the focus is the \emph{worst-subpopulation} accuracy.
We show in extensive experiments that U-AL outperforms algorithms that
explicitly aim to improve worst-subpopulation performance such as reweighting.
We provide insights that explain the good performance of U-AL and show a
theoretical result that is supported by our experimental observations.
Improving class and group imbalanced classification with uncertainty-based active learning
Alexandru Tifrea John Hill Fanny Yang