Introducing DART: A Novel Deep Adaptive Upsampling Technique for Handling Class Imbalance
Bibliographic record
Abstract
ABSTRACT Class imbalance remains a persistent challenge in predictive modeling, often leading to biased machine learning outcomes that disproportionately favor the majority class. This study investigates the effectiveness of advanced resampling techniques—both undersampling and oversampling—across two large and highly imbalanced datasets involving credit and loan default prediction. In addition to evaluating established oversampling techniques, the study introduces and validates a novel resampling approach, Deep Adaptive Resampling Technique (DART). Each technique is assessed using a consistent suite of classifiers, including logistic regression, gradient descent, naïve Bayes, random forest, CatBoost, and artificial neural networks. The results reveal that K‐MeansSMOTE and NearMiss outperform other resampling strategies in oversampling and undersampling, respectively, by achieving balanced trade‐offs in precision, recall, F1 score, AUC, and Matthews correlation coefficient. Notably, DART demonstrates exceptional performance across both datasets, achieving nearly perfect classification scores across all metrics, suggesting strong generalizability and robustness. The study further analyzes the strengths and limitations of each resampling technique and emphasizes the importance of metric selection when evaluating imbalanced datasets. By integrating empirical evaluation with theoretical insights, this research contributes to the growing body of literature on imbalanced learning and offers practical guidance for selecting appropriate resampling strategies. These findings have broader implications for domains such as finance, healthcare, and fraud detection, where class imbalance is common. Overall, the study affirms the value of hybrid and adaptive resampling methods in building more accurate and generalizable predictive models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".