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.

By Zeyu Dong, Jiahui Zhong
arXiv Machine Learning
Aug 19

RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network

RoBell-RVFL is a lightweight, quality‑aware generalized bell random vector functional link network designed to address class imbalance and noisy data in real‑world datasets. It uses a dual‑strategy sample‑level weighting: unit weights preserve minority class information, while a probability‑weighted generalized bell membership function suppresses noisy majority samples in a kernel‑induced feature space. Experiments on UCI and KEEL benchmarks, including tests with up to 40% label noise, show that RoBell‑RVFL consistently outperforms recent RVFL variants, demonstrating the importance of adaptive, quality‑aware sample weighting for robust learning.

By A. Rahaman, A. Quadir, M. Tanveer