Adaptive selection of ensembles for imbalanced class distributions
Bibliographic record
Abstract
Boolean combination (BC) techniques have been shown to efficiently integrate the responses of multiple classifiers in the ROC space for improved accuracy and reliability. Although the impact on classification performance of imbalanced class distributions may be addressed using ensemble-based techniques, it is difficult to observe with ROC curves. Given a false alarm rate and class imbalance, performing BC in the Precision-Recall Operating Characteristic (PROC) space can lead to a higher level of performance. In practice, class distributions often change over time, and BCs should adapt to reflect operational conditions. Thus, this paper proposes an adaptive system that initially uses skewed data to generate several BCs in the PROC space. Then, during operations, the class imbalance is periodically estimated, and used to estimate the most accurate BC of classifiers among operational points of these curves. Simulation results indicate that this approach maintains a level of accuracy that is comparable to full Boolean re-combination, but for a significantly lower computational cost.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".