WXGCB: A Clustering Prior Weighting Semi-Supervised Learning Method Based on Space Level Constraint and Mixed Variable Metrics
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Bibliographic record
Abstract
A clustering prior weighted semi-supervised learning method called WXGCB has been proposed, which combines the characteristics of the cluster-then-label semi-supervised method and space-level constraint semi-supervised method. WXGCB can use mixed variable information, data prior information, and clustering prior information based on different clustering algorithms to adjust the distance matrix, thereby transforming different supervised learning algorithms into semi-supervised learning algorithms for improving their prediction accuracy. Due to the fact that WXGCB does not require internal adjustments to the clustering algorithms and supervised learning algorithms used, this method can flexibly combine different clustering algorithms and supervised learning algorithms to find combinations that can better compensate for each other's shortcomings, and can easily convert various supervised learning algorithms into semi-supervised learning algorithms. To verify the effectiveness of WXGCB, WXGCB transformed two supervised learning algorithms KSNN and DBGLM into semi-supervised mixed variable learning algorithms SMKSNN and SMGLM, and conducted performance comparison experiments with the other two semi-supervised learning algorithms on six benchmark datasets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it