GAM Oversampling and GMM Based Resampling Algorithm for Classification of Imbalanced Sensitive Credit Data Sets
Bibliographic record
Abstract
Class imbalance remains a significant challenge in many datasets, whereby one class has considerably fewer samples than another, making it difficult to analyze and conduct research based on that dataset. It deals with resampling methodologies, especially oversampling and undersampling techniques, which are used to even out the class labels for imbalanced datasets. In particular, we introduce a new hybrid resampling technique based on Generative Adversarial Model Oversampling (GAMO) and Gaussian Mixture Model (GMM)-based grouping. The hybrid method is tested against standard approaches like SMOTE. This method gives not just higher accuracy in resampling data but also accounts for the protection of sensitive data, leaving vulnerabilities that are usually found in conventional methods. In this work, we demonstrate experimentally that our GAMOGMM hybrid approach can improve the representativeness and robustness of numerical datasets, which in turn strengthens the performance of deep learning models in impactful applications.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".