Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance

Trent Geisler (), Herman Ray () and Ying Xie ()
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Trent Geisler: United States Military Academy
Herman Ray: Kennesaw State University
Ying Xie: Kennesaw State University

Journal of Classification, 2023, vol. 40, issue 1, No 9, 192-212

Abstract: Abstract Imbalanced learning problems typically consist of data with skewed class distributions, coupled with large misclassification costs for the rare events. For binary classification, logistic regression is a common supervised learning technique chosen to perform this task. Unfortunately, the model performs poorly on classification tasks when class distributions are highly imbalanced. To improve this generalization, we implement a novel instance-level weighting methodology for the minority class in the loss function. We build our method from a recently published, locally weighted log-likelihood objective function, where each of the minority class weights are learned from the data. We improve upon this previous approach by creating a convex and hyperparameter-free loss function that improves generalization performance for datasets exhibiting extreme class imbalance.

Keywords: Statistical machine learning; Imbalanced learning; Logistic regression; Binary classification; Weighted loss function (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00357-023-09431-5

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