Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity
Bibliographic record
Abstract
High-dimensional sparse data with multicollinearity is frequently found in medical data. This problem can lead to poor predictive accuracy when applied to a new data set. The Least Absolute Shrinkage and Selection Operator (Lasso) is a popular machine-learning algorithm for variable selection and parameter estimation. Additionally, the adaptive Lasso method was developed using the adaptive weight on the l1-norm penalty. This adaptive weight is related to the power order of the estimators. Thus, we focus on 1) the power of adaptive weight on the penalty function, and 2) the two-stage variable selection method. This study aimed to propose the relaxed adaptive Lasso sparse logistic regression. Moreover, we compared the performances of the different penalty functions by using the mean of the predicted mean squared error (MPMSE) for the simulation study and the accuracy of classification for a real-data application. The results showed that the proposed method performed best on high-dimensional sparse data with multicollinearity. Along with, for classifier with the support vector machine, this proposed method was also the best option for the variable selection process.
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How this classification was reachedexpand
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 | 0.010 | 0.098 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".