arXiv Machine Learning
Density-Ratio Rescoring for Imbalanced Classification
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 23
Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Density‑Ratio Rescoring (DRR) enhances a classifier trained with the original class prior by adding a survey‑raking dual score that reweights the majority class to match minority feature moments within a tolerance. The method standardizes both the dual and base scores, combines them with equal weight, and uses the fitted dual directly for prediction without resampling or refitting the base model. Experiments on 24 tabular benchmarks and a gene‑expression cohort show that DRR improves average precision over the standardized base on every dataset, with a mean gain of 0.034, and outperforms a shared‑dual raking‑and‑relabeling resampler on most datasets.
arXiv Machine Learning
Sep 22
Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions
The paper benchmarks six long‑tail loss functions—cross‑entropy, weighted CE, class‑balanced loss, focal loss, LDAM, and logit‑adjusted softmax—across three single‑cell foundation model architectures (scGPT, scBERT, Geneformer) and three datasets (Multiple Sclerosis, Zheng68K, human Pancreas). It shows that overall accuracy masks systematic failures on rare, disease‑relevant cell types, with a consistent gap between overall accuracy, Macro‑F1, and rare‑class recall under plain cross‑entropy. The study identifies two distinct regimes of rare‑class failure, predicts reweighting efficacy by absolute training‑set size, and finds class‑balanced loss and LDAM to be the most reliable across all settings.
arXiv Machine Learning
5d ago
Beyond Scarcity: When Encoder Adaptation Is Worthwhile
The paper introduces a controlled evaluation framework to determine when encoder adaptation is beneficial after correcting for class imbalance in long‑tailed recognition tasks. By separating unique support from representation allocation and comparing classifier‑only versus full‑network continuations, the study shows that encoder updates become worthwhile only when representation allocation is highly imbalanced, a transition that persists across various corrections and scales to ImageNet. The framework uses stage‑specific controls, multiple objectives, whole‑distribution metrics, and hierarchical inference to robustly estimate incremental encoder value.
arXiv AI
Jun 16
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
arXiv Machine Learning
Jul 15
Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
arXiv:2604. 01506v2 Announce Type: replace Abstract: Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference time.
arXiv Machine Learning
Jun 30
The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles
arXiv:2606. 29720v1 Announce Type: new Abstract: Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by minority-class accuracy or F1.