Theoretically Grounded Adaptive Sampling for Imbalanced Multi-Label Classification in Medical Text
Year: 2025
Type: article
Abstract: Multi-label medical text classification faces severe class imbalance, where rare pathological conditions appear hundreds of times less frequently than common findings. This imbalance causes standard neural networks to achieve high overall accuracy while failing to detect rare but clinically important conditions. Existing sampling strategies rely on heuristic rules rather than theoretical foundations and treat all minority classes uniformly despite their varying degrees of rarity. We propose two adaptive sampling strategies: a rule-based method using percentile-based categorization and a formula-based method grounded in Vapnik-Chervonenkis (VC) dimension theory. The formula-based approach derives upsampling weights throughwc= ln(rc+ 1) · αc, combining logarithmic scaling of imbalance ratios with smooth interpolation. This formulation aligns upsampling decisions with sample complexity requirements from statistical learning theory while preventing overfitting. Experiments on the PadChest chest X-ray report dataset demonstrate that the formula-based method achieves 31.9% improvement in Macro F1 with 34.5% fewer training samples than rule-based sampling. The method reduces the F1 gap from 0.51 to 0.42, demonstrating significantly improved balance between rare and common class performance. Results validate that theoretically-motivated sampling strategies yield measurable benefits over heuristic approaches.
Source: IEEE Access
Authors: Kyung Won Cho, Sungkwan Youm, Kwang-Seong Shin
Institutions: Nanyang Technological University, Sunchon National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4417201614