AIFS: A novel perspective, Artificial Intelligence infused wrapper based Feature Selection Algorithm on High Dimensional data analysis
Bibliographic record
Abstract
Abstract Background Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an optimal feature set. However, these approaches underutilize the capability of feature subset models, which impacts feature selection and its predictive performance. Method and Results This study proposes a novel Artificial Intelligence infused wrapper based Feature Selection (AIFS), a new feature selection method that integrates artificial intelligence with wrapper based feature selection. The approach creates a Performance Prediction Model (PPM) using artificial intelligence (AI) which predicts the performance of any feature set and allows wrapper based methods to predict and evaluate the feature subset model performance without building actual model. The algorithm can make wrapper based method more relevant for high-dimensional data and is flexible to be applicable in any wrapper based method. We evaluate the performance of this algorithm using simulated studies and real research studies. AIFS shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms like LASSO and sparse partial least squares. Conclusion AIFS approach provides an alternative method to the existing approaches for feature selection. The current study focuses on AIFS application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".