Distance-Based Relevance Function for Imbalanced Regression

Daniel Daeyoung In and Hyunjoong Kim ()
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Daniel Daeyoung In: Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
Hyunjoong Kim: Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea

Stats, 2025, vol. 8, issue 3, 1-14

Abstract: Imbalanced regression poses a significant challenge in real-world prediction tasks, where rare target values are prone to overfitting during model training. To address this, prior research has employed relevance functions to quantify the rarity of target instances. However, existing functions often struggle to capture the rarity across diverse target distributions. In this study, we introduce a novel Distance-based Relevance Function (DRF) that quantifies the rarity based on the distance between target values, enabling a more accurate and distribution-agnostic assessment of rare data. This general approach allows imbalanced regression techniques to be effectively applied to a broader range of distributions, including bimodal cases. We evaluate the proposed DRF using Mean Squared Error (MSE), relevance-weighted Mean Absolute Error ( MAE ϕ ), and Symmetric Mean Absolute Percentage Error (SMAPE). Empirical studies on synthetic datasets and 18 real-world datasets demonstrate that DRF tends to improve the performance across various machine learning models, including support vector regression, neural networks, XGBoost, and random forests. These findings suggest that DRF offers a promising direction for rare target detection and broadens the applicability of imbalanced regression methods.

Keywords: imbalanced regression; relevance function; distance-based relevance; rare target (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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