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.
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Although lif...
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.
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
The paper introduces a bias‑correction method for synthetic oversampling in imbalanced learning. It estimates the loss discrepancy caused by the data generator using a held‑out majority subset and transfers this correction to the minority class under a uniform bias‑transfer assumption. The authors provide finite‑sample bounds for bias transfer and excess balanced risk, identify when SMOTE introduces significant bias, and demonstrate the method’s applicability to multi‑task learning and propensity‑score estimation, with empirical results showing greatest benefit when synthetic distortion is large.
arXiv:2606. 26053v1 Announce Type: cross Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood.
Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score.
arXiv:2508. 10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.
arXiv:2604.22391v2 Announce Type: replace-cross Abstract: The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive...
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:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.