Density-Ratio Rescoring for Imbalanced Classification

2026-09-20

AI Summary

该研究通过密度比重评分（DRR）方法解决不平衡分类问题，利用调查整合法对多数样本重新加权，并结合基础分类器提高稀有类排名的精度。

Abstract

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.

Problem

• Imbalanced Classification
• Density-Ratio Rescoring
• Minority Class
• Raking
• Dual Score

Innovation

• Density-Ratio Rescoring
• Imbalanced Classification
• Survey Raking
• Dual Score
• Minority Class