An Empirical Study of Supervised and Active Learning Methodologies for Classifying Research Papers
Bibliographic record
Abstract
Document classification is one of the predominant tasks in Machine Learning, that by and large involves the use of supervised methodologies to determine most suitable categories for a given set of documents. In this project, we seek to conduct an empirical study, that, in addition to the conventional machine learning methods, experiments with active learning and semi-supervised methods for assorting a mix of labelled and unlabelled computer science scholarly articles hosted on the arXiV repository. We develop a solution using the gathered labelled data points, by experimenting with various multi-class supervised learning models such as K Nearest Neighbours, Naive Bayes, Random Forest, and Logistic Regression. We also account for the issue of class imbalance, and thus conduct experiments to determine a suitable oversampling methodology for addressing this concern. Lastly, we extend beyond the conventional machine learning classification methods to incorporate the concept of active learning, a type of iterative supervised learning, used when the unlabelled data is in abundance.
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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 | 0.078 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".