The use of unlabelled data for supervised learning
Bibliographic record
Abstract
When provided with enough labelled training examples, a supervised learning algorithm can learn reasonably accurately. However, creating sufficient labelled data to train accurate classifiers is time consuming and expensive. On the other hand, unlabelled data is usually easy to obtain. This research introduces a novel approach, Guelph Cluster Class (GCC), which improves the task of classification with the use of unlabelled data. The novelty of this approach lies in the use of an unsupervised network, 'Self-Organizing Map', to select natural clusters in labelled and unlabelled data. Sub-classes (made by labelled data) are used to assign labels to unlabelled patterns to produce ' self-labelled' data. The performance of several variants of the GCC system have been obtained by running a 'Back-Propagation' network on labelled and self-labelled data. Results of experiments on several benchmark datasets demonstrate an increasing power for the classification procedure even when the number of labelled data is very small.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".