Synthetic Data Generation for Alleviating Class Imbalance with Reinforcement Learning
Bibliographic record
Abstract
Wearable sensors are increasingly used in daily life for applications such as fitness monitoring, rehabilitation, and workplace safety. Human activity recognition (HAR) models play a central role in these systems by classifying movement patterns. Nevertheless, class imbalance reduces model reliability, particularly in health-related contexts. This paper proposes an adaptive reinforcement learning framework for synthetic data generation that operates as a feedback-driven process. In each training episode, the agent generates minority-class samples, a reference classifier evaluates their utility, and the policy is updated based on both the improvement in minority-class performance and the proximity of samples to decision boundaries. Experimental results show that the proposed method improves minority-class F1 scores over random undersampling, random oversampling, and generative adversarial network baselines, particularly in low-separability scenarios where class overlap is significant. These results demonstrate that feedback-based adaptive synthesis effectively addresses imbalance by targeting underrepresented regions in the feature space, leading to fairer and more reliable activity recognition.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".