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A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use independence-driven importance weighting for feature selection instead of the compared filter, wrapper, and embedded methods | Synthetic covariate-shift feature selection · Y_MLP and Y_poly | F1 score | Helped |
| Use SRDO rather than DWR for feature selection with a complex nonlinear target | Independence-driven importance weighting · Y_MLP, especially at large training bias rates | F1 score | Helped |
| Use DWR for feature selection with a polynomial target | Independence-driven importance weighting · Y_poly | F1 score | Helped |
| Increase the strength of spurious correlations in the training data | All feature selection methods · Synthetic data, Y_MLP and Y_poly | F1 score | Hurt |
| Select the minimal stable variable set rather than a different number of top-ranked features | SRDO followed by MLP regression · Covariate-shift test environments | RMSE | Helped |
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