Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data. (arXiv:2210.13958v1 [cs.LG])

Several approaches have been developed to mitigate algorithmic bias stemming
from health data poverty, where minority groups are underrepresented in
training datasets. Augmenting the minority class using resampling (such as
SMOTE) is a widely used approach due to the simplicity of the algorithms.
However, these algorithms decrease data variability and may introduce
correlations between samples, giving rise to the use of generative approaches
based on GAN. Generation of high-dimensional, time-series, authentic data that
provides a wide distribution coverage of the …

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