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The paper introduces SMOTE‑VAR, an uncertainty‑aware oversampling technique that uses a Gaussian process variance function to filter out invalid synthetic minority samples. Applied to a university student depression dataset, SMOTE‑VAR outperforms existing oversampling methods in predicting remission outcomes. By more accurately identifying students unlikely to remit, the method supports earlier, personalized interventions.
The paper introduces a new oversampling technique that uses a predictive model to generate nominal variables instead of interpolating them, addressing a key limitation of SMOTE. Applied to a large GPS‑location dataset of university students, the method outperforms existing oversampling approaches in predicting high levels of amotivation and anhedonia. This improvement could help identify students with these debilitating symptoms earlier and more accurately.
arXiv:2505. 13518v3 Announce Type: replace-cross Abstract: Imbalanced datasets, where one class significantly outnumbers others, remain a persistent challenge in machine learning, often biasing predictions toward the majority class and degrading classifier performance.
arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.
The paper investigates how uncertainty quantification fails in selective prediction systems when clinical data exhibit severe class imbalance, leading to systematic exclusion of minority patients. Through experiments on 56 synthetic scenarios and three neurodegenerative disease cohorts, the authors show that standard uncertainty measures become proxies for class membership rather than true ambiguity, and that combining class‑aware correction (CCR) with threshold‑aware metrics restores proper monotonicity and protects minority patients. The study proposes adopting threshold‑aligned uncertainty measures and class‑stratified evaluation to mitigate this bias and improve safety in clinical AI pipelines.
arXiv:2610.08809v1 Announce Type: new Abstract: Depression severity among patients with chronic or acute medical conditions is influenced by a complex interaction of baseline psychological state, dem...