Enhancing Model Performance in Hybrid Class Imbalance Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Class imbalance is a crucial issue in real world scenarios. Traditional classifiers trained with imbalanced datasets become biased towards the majority class (class with more instances), resulting in misclassification. Therefore, it is essential to have uniform class distribution within the dataset. Ensemble methods have gained more attention from the researchers for managing the issue of imbalance distribution of classes. In this paper, three hybrid approaches are implemented using Over-sampling technique (Adaptive Synthetic Sampling- ADASYN) and Ensemble methods: Bagging, Boosting and Stacking. The performance of these hybrid approaches (ADASYN-Bagging, ADASYN-Boosting and ADASYN-Stacking) is evaluated using four performance metrics Accuracy (ACC), F 1 -score, Geometric-mean (GM) and Receiver operator characteristics Area under the ROC curve (ROC-AUC) on twelve imbalanced datasets taken from KEEL repository. The study suggest that ADASYN-Stacking outperforms all other approaches with ROC-AUC value 99.93%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it