Improving Class Imbalance in the Classification of Multi-Dimensional Data: Interpretable Model Design and Evaluation
Bibliographic record
Abstract
This study presents a hybrid approach that combines deep learning techniques with conventional machine learning techniques to address the class imbalance in the classification of multi-dimensional data. The resulting framework incorporates SHapley Additive exPlanations (SHAP) to evaluate the model predictions based on domain knowledge. It combines Conditional Generative Adversarial Networks (CGANs), Self-Supervised Clustered GANs (SSCGANs), and Variational Autoencoders (VAEs) for the generation of improved synthetic data. This method ensures that model decisions are based on domain-specific knowledge while enabling efficient computation of SHAP values by approximation of complex classifiers using surrogate models. Evaluations show that the suggested method overcomes the shortcomings of current techniques in high-stakes domains and improves classification performance and transparency.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".