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 ensemble with an unmitigated model limits the loss on frequent samples.
Imbalance in Regression Datasets
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abstract
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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Model-agnostic Mitigation Strategies of Data Imbalance for Regression
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 ensemble with an unmitigated model limits the loss on frequent samples.