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A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification

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arXiv:2605. 14147v1 Announce Type: new Abstract: Objective: The primary goal of this study was to systematically examine the impact of commonly used imbalance handling methods (IHMs) on predictive performance in biomedical binary classification, considering the interplay between model complexity and diverse data modalities.

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Authors: Jiandong Chen, Lingjie Su, Le Peng, Yash Travadi, Rui Zhang, Ju Sun
Released: 2026-05-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, rect, under, eval, line, rate, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.14147

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