OSK: Optimal Subsampling Method Based on K-means Clustering for Imbalanced Big Data
Bibliographic record
Abstract
Abstract While existing methodologies have effectively addressed big data subsampling, there is a noticeable gap in research concerning imbalanced subsampled data. To tackle the unique challenge of low specificity in the minority class within highly imbalanced datasets, we introduce the Optimal Subsampling technique rooted in K-means clustering (OSK). This method is specifically designed for massive yet imbalanced datasets. Our proposed approach employs an optimal subsampling mechanism to extract representative subsamples and utilizes K-means clustering to transform imbal-anced subsamples into multiple balanced datasets. In the spirit of ensemble learning, the OSK method generates predictions by averaging predicted values from multiple models and subsequently derives categorical decisions. The effectiveness of the OSK method is substantiated through extensive simulation studies and a real-world data example. Its superiority is evident in terms of shorter running time and higher classification accuracy when compared to existing methods.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".