A group-classification plus multi-expert regression framework with symmetric descending soft labels improves deep imbalanced regression on age and text-similarity benchmarks.
Data Augmentation for Imbalanced Regression
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abstract
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can lead to biases in the estimates. In this case, we propose a data augmentation algorithm that combines a weighted resampling (WR) and a data augmentation (DA) procedure. In a first step, the DA procedure permits exploring a wider support than the initial one. In a second step, the WR method drives the exogenous distribution to a target one. We discuss the choice of the DA procedure through a numerical study that illustrates the advantages of this approach. Finally, an actuarial application is studied.
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cs.LG 1years
2024 1verdicts
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Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
A group-classification plus multi-expert regression framework with symmetric descending soft labels improves deep imbalanced regression on age and text-similarity benchmarks.