Sweetening the Dataset : Using Active Learning to Label Unlabelled Datasets
Bibliographic record
Abstract
Supervised machine learning approaches assume the existence of a large collection of manually labelled examples of the problem under consideration. However, in many cases such a collection does not exist and creating one is time consuming and expensive. This can be a barrier to the use of supervised learning in certain situations, particularly when the doubt as to whether the system will work or not makes the cost of creating a dataset unjustifable. Active learning is a machine learning technique that has been used widely to create classification systems in the absence of large numbers of labelled examples, but that can also be used to create such collections. This paper will describe a system that uses active learning to label large collections of unlabelled data. We will show that the system can create an accurately labelled dataset aproximately 10 times the size of the set of examples manually labelled by an expert. The experiments described are based on recipe data from the 1st Computer Cooking Contest to be held at ECCBR'08 and focus on identifying those recipes in the set that are desserts.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| 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".