A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

Min Du; Nesime Tatbul; Brian Rivers; Akhilesh Kumar Gupta; Lucas Hu; Wei Wang; Ryan Marcus; Shengtian Zhou; Insup Lee; Justin Gottschlich

arXiv · 2010.05995

A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

Min Du·Nesime Tatbul·Brian Rivers·Akhilesh Kumar Gupta·Lucas Hu·Wei Wang·Ryan Marcus·Shengtian Zhou·Insup Lee·Justin Gottschlich

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Abstract

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.

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Min Du, Nesime Tatbul, Brian Rivers, Akhilesh Kumar Gupta, Lucas Hu, Wei Wang, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich. 2023-11-16. A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification. https://arxiv.org/abs/2010.05995

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