Distribution Enhancement for Imbalanced Data with Generative Adversarial Network
Bibliographic record
Abstract
Abstract Tackling imbalanced problems encountered in real‐world applications poses a challenge at present. Oversampling is a widely useful method for imbalanced tabular data. However, most traditional oversampling methods generate samples by interpolation of minority (positive) class, failing to entirely capture the probability density distribution of the original data. In this paper, a novel oversampling method is presented based on generative adversarial network (GAN) with the originality of introducing three strategies to enhance the distribution of the positive class, called GAN‐E. The first strategy is to inject prior knowledge of positive class into the latent space of GAN, improving sample emulation. The second strategy is to inject random noise containing this prior knowledge into both original and generated positive samples to stretch the learning space of the discriminator of GAN. The third one is to use multiple GANs to learn comprehensive probability distributions of positive class based on multi‐scale data to eliminate the influence of GAN on generating aggregate samples. The experimental results and statistical tests obtained on 18 commonly used imbalanced datasets show that the proposed method comes with a better performance in terms of G‐mean, F‐measure, AUC and accuracy than 14 other rebalanced methods.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".