Robust Semi-Supervised Feature Selection With Multi-Granularity Zentropy Modeling
Bibliographic record
Abstract
High-dimensional and weakly supervised (HiDWS) data present significant challenges for traditional machine learning and pattern recognition. Although semi-supervised feature selection has shown effectiveness in improving the quality of HiDWS data, existing methods remain sensitive and lack robustness due to the unreliability of unlabeled data learning and the uncertainty in modeling processes. Hence, this study focuses on a multi-granularity zentropy modeling (Ze-MGM) framework with model-agnostic for highly-accuracy and robust semi-supervised feature selection. Unlike existing methods, Ze-MGM does not rely on specific settings such as rough or fuzzy set assumptions and can effectively capture the granularity of information under high-dimensional and weakly supervised data scenarios. Specifically, we first introduce a strategic soft label ($S2-$S2-Label) learning method that integrates object proximity and classification certainty to reduce uncertainty between features and labels. This method also enables the selection of compatible instances, thereby mitigating the negative impact of incompatible objects on label learning. Subsequently, a multi-granularity knowledge space and zentropy uncertainty measure are constructed by analyzing the hierarchical relationships among labels, decisions, and specific classes, which enables accurate multi-granularity knowledge representation and multi-granularity uncertainty characterization in HiDWS data modeling processing. Finally, two multi-granularity significance measures based on multi-granularity uncertainty are defined for feature evaluation and selection via a semi-supervised paradigm. Extensive experiments on multiple benchmark datasets demonstrate that the proposed Ze-MGM method achieves superior generalization performance and robustness compared to state-of-the-art 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".