Continuous Weight Balancing
This addresses data imbalance issues in machine learning, but it is incremental as it builds on existing weighting approaches.
The authors tackled the problem of imbalanced or skewed data by proposing a method to derive sample weights from the transfer function between estimated source and target distributions, outperforming unweighted and discretely-weighted models on regression and classification tasks.
We propose a simple method by which to choose sample weights for problems with highly imbalanced or skewed traits. Rather than naively discretizing regression labels to find binned weights, we take a more principled approach -- we derive sample weights from the transfer function between an estimated source and specified target distributions. Our method outperforms both unweighted and discretely-weighted models on both regression and classification tasks. We also open-source our implementation of this method (https://github.com/Daniel-Wu/Continuous-Weight-Balancing) to the scientific community.