Spectral graph-based semi-supervised learning for imbalanced classes
Bibliographic record
Abstract
Semi-supervised learning makes the realistic assumptions that labelled data is typically rare, and that unlabelled data that are are likely to belong to the same class. Unlabelled data are assigned the labels associated with their most similar labelled neighbors. For graph-based semi-supervised learning, most similar' is defined by weighted multipath path length in a graph. When classes are of different sizes, or the number of labelled nodes per class is not the same across classes, the performance of existing graph-based algorithms degrades sharply.We develop a new algorithm that creates representative nodes for each class, connects them to the labelled nodes of that class, adds negative edges between them, embeds the resulting graph using a signed graph Laplacian technique, and then predicts the unlabelled nodes using distance-based techniques in the geometry of the embedding. Its performance matches current algorithms for balanced datasets, but is much better for datasets where the classes, or the number of labelled records, differ in size. Keywords: spectral graph embedding, signed graphs, semisupervised learning, Laplacians
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".