# A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

arXiv: https://arxiv.org/abs/2010.05995 · HTML: https://mlsanity.com/paper/2010.05995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders](https://mlsanity.com/paper/2511.17113/) (2025)

## Abstract (arXiv)

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics such as Balanced Accuracy are commonly used to evaluate a classifier's prediction performance under such scenarios. However, these metrics fall short when classes vary in importance. In this paper, we propose a simple and general-purpose evaluation framework for imbalanced data classification that is sensitive to arbitrary skews in class cardinalities and importances. Experiments with several state-of-the-art classifiers tested on real-world datasets from three different domains show the effectiveness of our framework - not only in evaluating and ranking classifiers, but also training them.