Learning from Imbalanced Data Sets: A Comparison of Various Strategies *
Bibliographic record
Abstract
Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exists many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in the case of connectionist systems, class imbalances hinder the performance of standard classifiers and 2) to compare the performance of several approaches previously proposed to deal with the problem. 1. Introduction As the field of machine learning makes a rapid transition from the status of "academic discipline" to that of "applied science", a myriad of new issues, not previously considered by the machine learning community, is now coming into light. One such issue is the class imbalance problem. The class imbalance problem corresponds to domains for which one class is represented...
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.005 |
| 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".