# Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores

arXiv: https://arxiv.org/abs/2609.23926 · HTML: https://mlsanity.com/paper/2609.23926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [CLIMB: Class-imbalanced Learning Benchmark on Tabular Data](https://mlsanity.com/paper/2505.17451/) (2025)
- [Tabular Imbalanced Learning: A Survey, Benchmark, and Practical Guide](https://mlsanity.com/paper/2605.14915/) (2026)

## Abstract (arXiv)

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 common within-class covariance. On 24 tabular benchmarks, evaluated over 30 trials and five base learners, DRR at the D=128 random-feature setting improves average precision over the standardized base on every dataset, with a mean gain of 0.034. It exceeds the shared-dual raking-and-relabeling resampler on 22 of 24 datasets, with a mean gain of 0.092, and on all eight one-versus-rest tasks of a shared gene-expression cohort. These results demonstrate the effectiveness of using raking duals as reusable scores for improving rare-class ranking while retaining classifiers trained at the original prior.