Data Augmentation for Imbalanced Regression

Our paper Data Augmentation for Imbalanced Regression, writen with Denys Pommeret and Sam Stocksieker, is now available on ArXiv.

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.



Cite this blog post
Arthur Charpentier (2023, February 23). Data Augmentation for Imbalanced Regression. Freakonometrics. Retrieved May 21, 2024, from https://doi.org/10.58079/ovln

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