Multi-class imbalanced semi-supervised learning from streams through online ensembles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-class imbalance, in which the rates of instances in the various classes differ substantially, poses a major challenge when learning from evolving streams. In this setting, minority class instances may arrive infrequently and in bursts, making accurate model construction problematic. Further, skewed streams are not only susceptible to concept drifts, but class labels may also be absent, expensive to obtain, or only arrive after some delay. The combined effects of multi-class skew, concept drift and semi-supervised learning have received limited attention in the online learning community. In this paper, we introduce a multi-class online ensemble algorithm that is suitable for learning in such settings. Specifically, our algorithm uses sampling with replacement while dynamically increasing the weights of underrepresented classes based on recall in order to produce models that benefit all classes. Our approach addresses the potential lack of labels by incorporating a self-training semi-supervised learning method for labeling instances. Our experimental results show that our online ensemble performs well against multi-class imbalanced data containing concept drifts. In addition, our algorithm produces accurate predictions, even in the presence of unlabeled data.
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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 | 0.000 |
| 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 it