Feature Selection via Independent Domination
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
Feature or variable selection is a fundamental problem in data analysis and statistical modeling. Classic methods resulting in dimensionality reduction are diverse and include things like statistical hypotheses testing for zero coefficients, ‘stepwise’ methods minimizing, e.g., AIC, the spectra of a data matrix or principal component analysis, regularization methods especially the Lasso, various heuristic and ‘shrinkage’ methods all of which result in a subset of the feature space used as a basis for statistical modeling. Combinatorial variable selection has also been used in a manner that aids in the selection of a good subset of the feature space. A graph, or the ‘data graph’, is based on the pairwise correlations of features and may be used to extract the most distinguishing features. Partly due to high computational cost, combinatorial variable selection methods have not been well studied. We consider a variable selection procedure via the Minimum Independent Dominating Set problem. We explore the use of some exact and heuristic methods that proved to be effective for feature extracting and ranking.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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