Improving Class and Group Imbalanced Classification with Uncertainty-Based Active Learning
Abstract
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.
Cite
Text
Tifrea et al. "Improving Class and Group Imbalanced Classification with Uncertainty-Based Active Learning." NeurIPS 2023 Workshops: ReALML, 2023.Markdown
[Tifrea et al. "Improving Class and Group Imbalanced Classification with Uncertainty-Based Active Learning." NeurIPS 2023 Workshops: ReALML, 2023.](https://mlanthology.org/neuripsw/2023/tifrea2023neuripsw-improving/)BibTeX
@inproceedings{tifrea2023neuripsw-improving,
title = {{Improving Class and Group Imbalanced Classification with Uncertainty-Based Active Learning}},
author = {Tifrea, Alexandru and Hill, John and Yang, Fanny},
booktitle = {NeurIPS 2023 Workshops: ReALML},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/tifrea2023neuripsw-improving/}
}