Adaptive threshold-based classification of sparse high-dimensional data
Bibliographic record
Abstract
We revisit the problem of designing an efficient binary classifier in a challenging high-dimensional framework. The model under study assumes some local dependence structure among feature variables represented by a block-diagonal covariance matrix with a growing number of blocks of an arbitrary, but fixed size. The blocks correspond to non-overlapping independent groups of strongly correlated features. To assess the relevance of a particular block in predicting the response, we introduce a measure of “signal strength” pertaining to each feature block. This measure is then used to specify a sparse model of our interest. We further propose a threshold-based feature selector which operates as a screen-and-clean scheme integrated into a linear classifier: the data is subject to screening and hard threshold cleaning to filter out the blocks that contain no signals. Asymptotic properties of the proposed classifiers are studied when the sample size n depends on the number of feature blocks b, and the sample size goes to infinity with b at a slower rate than b. The new classifiers, which are fully adaptive to unknown parameters of the model, are shown to perform asymptotically optimally in a large part of the classification region. The numerical study confirms good analytical properties of the new classifiers that compare favorably to the existing threshold-based procedure used in a similar context.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".