ReviewFrontiers in digital health2024
A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems.
Review in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Addressing Class Imbalance in Fetal Health Classification: Rigorous Benchmarking of Multi-Class Resampling Methods on Cardiotocography Data.Diagnostics (Basel, Switzerland) · 2026Article
- The Effect of Age and Use of Enamel Matrix Derivative on Implant Loss.Dentistry journal · 2026Article
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- Cross-national predictive correlates of depressive symptoms among middle-aged and older adults with chronic conditions: an explainable machine learning analysis.Frontiers in psychiatry · 2026Article
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- Magnetoencephalographic Spectral Abnormalities and Explainable Machine Learning of Neuropathic-Like Pain in Knee Osteoarthritis.Journal of pain research · 2026Article
- IFC-HFlowVAE: A self-enhancing generative framework with structural anchoring for imbalanced clinical data augmentation.PloS one · 2026Article
- Radiomics-based classification of pediatric dental trauma in periapical radiographs: a preliminary study.BMC medical imaging · 2025Article
- Physiological, Psychological, and Functional Health Determinants of Depressive Symptoms Among the Elderly in India: Evaluation of Classification Performance of XGBoost Models.Indian journal of psychological medicine · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.