Semiparametric Subsampling and Data Condensation for Large-Scale Data Analytics
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
Subsampling is often used to reduce the complexity of large datasets. However, such methods need to ensure that the subsampled data are representative of the original dataset. Here, we introduce a new clustering-based data condensation (subsampling) framework for large datasets. The framework relies on the use of stratified sampling, Voronoi diagrams, and variational Bayes-based Gaussian mixture clustering. We tested the proposed framework on three large imbalanced benchmark datasets, namely cod-RNA, ds1.10, and ds1.100. The efficiency and generality of the proposed framework were assessed by comparing the predictive performance of the reduced datasets with the original datasets over two machine-learning classifiers, namely the random forest, and the radial basis function network. The evaluation metrics included the accuracy, F-measure and reduction percentage. We found that very high reduction percentages can be achieved using our new framework while maintaining satisfactory predictive performance.
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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 | 0.001 | 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