Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Density-Ratio Rescoring (DRR) augments a classifier trained at the original class prior with a survey-raking dual score. Raking reweights the majority sample to match minority feature moments within a tolerance. DRR marginally standardizes the dual and base scores and combines them with a fixed weight of one half, using the fitted dual directly for prediction without resampling or refitting the base classifier. Under exact population matching and a correctly specified log-linear tilt model, the dual equals the log density ratio up to an additive constant. A class-separation analysis characterizes the signal strength and correlation conditions under which fusion improves separation under c...
| Authors | Dongha Kim, Seunghwan Park |
|---|---|
| Primary category | stat.ML |
| Published | 20 September 2026 |
| Last updated at source | 22 September 2026 |
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