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Imbalance in Regression Datasets

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arxiv 2402.11963 v1 pith:AUKRHC7O submitted 2024-02-19 cs.LG cs.AI

Daniel Kowatsch , Nicolas M. Müller , Kilian Tscharke , Philip Sperl , Konstantin Bötinger This is my paper · ORCID
classification cs.LGcs.AI
keywords imbalanceproblemregressionbeenclassificationdataoverlookedtraining
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For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important problem which has so far been overlooked: Due to under- and over-representations in a data set's target distribution, regressors are prone to degenerate to naive models, systematically neglecting uncommon training data and over-representing targets seen often during training. We analyse this problem theoretically and use resulting insights to develop a first definition of imbalance in regression, which we show to be a generalisation of the commonly employed imbalance measure in classification. With this, we hope to turn the spotlight on the overlooked problem of imbalance in regression and to provide common ground for future research.

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Cited by 3 Pith papers

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  1. Instance Hardness-Based Relevance for Imbalanced Regression

    cs.LG 2026-07 conditional novelty 6.0 of 10

    An instance-hardness relevance function that labels examples by prediction difficulty improves oversampling for imbalanced regression, with modest empirical gains.

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  3. Model-agnostic Mitigation Strategies of Data Imbalance for Regression

    cs.LG 2025-06 conditional novelty 4.0 of 10

    The paper proposes two relevance functions and two sampling methods for imbalanced regression, and reports that crbSMOGN with density-ratio relevance improves rare-sample prediction for neural networks, while an ensem...

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