A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

Understand

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.