Fully Bayesian Classification with Heavy-tailed Priors for Selection in\n High-dimensional Features with Grouping Structure
Bibliographic record
Abstract
Feature selection is demanded in many modern scientific research problems\nthat use high-dimensional data. A typical example is to find the most useful\ngenes that are related to a certain disease (eg, cancer) from high-dimensional\ngene expressions. The expressions of genes have grouping structures, for\nexample, a group of co-regulated genes that have similar biological functions\ntend to have similar expressions. Many statistical methods have been proposed\nto take the grouping structure into consideration in feature selection,\nincluding group LASSO, supervised group LASSO, and regression on group\nrepresentatives. In this paper, we propose a fully Bayesian Robit regression\nmethod with heavy-tailed (sparsity) priors (shortened by FBRHT) for selecting\nfeatures with grouping structure. The main features of FBRHT include that it\ndiscards more aggressively unrelated features than LASSO, and it can make\nfeature selection within groups automatically without a pre-specified grouping\nstructure. In this paper, we use simulated and real datasets to demonstrate\nthat the predictive power of the sparse feature subsets selected by FBRHT are\ncomparable with other much larger feature subsets selected by LASSO, group\nLASSO, supervised group LASSO, penalized logistic regression and random forest,\nand that the succinct feature subsets selected by FBRHT have significantly\nbetter predictive power than the feature subsets of the same size taken from\nthe top features selected by the aforementioned methods.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".