Coverage-performance curves for classification in datasets with a typical data
Bibliographic record
Abstract
Handling atypical examples in classification tasks is one of the challenges in machine learning. While there seems to be a race for accuracy, very little has been done to understand and solve the issues related to atypical data. In this paper, coverage-performance (CP) curves are introduced to help a better understanding of atypical data. The concept of CP curves is based on the idea of separating atypical data and visualizing performance of classification as a function of coverage (the fraction of data participating in training or evaluation). To generate CP curves, two schemes are compared in this paper. The first scheme is based on SVMs alone and the second one is a hybrid of a PNN and a SVM. Two generated datasets with overlapping features are used to demonstrate the effectiveness of CP curves obtained by each scheme. Calculated theoretical limits on the generated data show that the hybrid scheme is a very effective way of producing CP curves. It is also shown that by separating atypical data, although we lose some data, the performance of the classification increases significantly.
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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 | 0.016 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".