Oversampling With GAN via Meta-Learning for Imbalanced Data
Bibliographic record
Abstract
Utilizing generative adversarial networks (GANs) for oversampling imbalanced data has demonstrated its effectiveness. However, many GAN-based oversampling methods are confronted with a significant challenge, namely, mode collapse, especially when dealing with tabular imbalanced data. In this paper, two unique penalty terms are respectively incorporated into the loss functions of the discriminator and the generator of GAN to promote the generated samples to exhibit not just statistical but also spatial information consistency with the minority samples, thereby alleviating the issue of mode collapse. In contrast to other studies that fix the coefficient of the penalty terms, the optimal coefficients of the penalty terms are adaptively searched using a meta-learning approach, where Bayesian optimization is firstly employed to effectively handle situations involving small size of minority samples in the imbalanced data. We call the proposed model as META_GAN. Experimental results demonstrate that META_GAN outperforms alternative oversampling methods on general tabular and image imbalanced datasets and long-tailed datasets in terms of different metrics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".