High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) Algorithm in Classifying Height Indicators Through Social-life and Well-being Factors
Bibliographic record
Abstract
Introduction: High dimensional Selection with Interactions for Binary Outcome (HDSI-BO) algorithm can incorporate interaction terms and combine with existing techniques for feature selection. Simulation studies have validated the ability of HDSI-BO to select true features and consequently, improve prediction accuracy compared to standard algorithms. Our goal is to assess the applicability of HDSI-BO in combining different techniques and measure its predictive performance in a real data study of predicting height indicators by social-life and well-being factors. Methods: HDSI-BO was combined with logistic regression, ridge regression, LASSO, adaptive LASSO, and elastic net. Two-way interaction terms were considered. Hyperparameters used in HDSI-BO were optimized through genetic algorithms with five-fold cross-validation. To measure the performance of feature selection, we fitted final models by logistic regression based on the sets of selected features and used the model’s AUC as a measure. 30 trials were repeated to generate a range of the number of selected features and a 95% confidence interval for AUC. Results: When combined with all of the above methods, HDSI-BO methods achieved higher final AUC values both in terms of mean and confidence interval. In addition, HDSI-BO methods effectively narrowed down the sets of selected features and interaction terms compared with standard methods. Conclusion: The HDSI-BO algorithm combines well with multiple standard methods and has comparable or better predictive performance compared with the standard methods. The computational and time complexity of HDSI-BO is higher but still acceptable. Considering AUC as the single metric cannot comprehensively measure the feature selection performance. More effective metrics of performance should be explored for future work.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".