5 - Stratégies de rejet en classification supervisée : une synthèse par opérateurs de De Morgan
Bibliographic record
Abstract
In this article we review strategies used in the design of two-folded rejection-based classifiers. Beside the so-called classical “accept-first” strategy we have recently proposed very general families built on two different approaches, namely the “reject-first” [Fre98a, MF01b] and “mixture-first” [SFM02] reject schemes. These three approaches differ by the kind, as well as the order, of the tests leading to the classifier final output. While the first one starts by testing for distance rejection and, if necessary, finishes by testing for exclusive classification or ambiguity rejection respectively, the two others start respectively by testing for exclusive classification and ambiguity rejection, and then finish by the remaining alternatives. We unify the three schemes by defining fuzzy operators built on De Morgan operators (t-norms, t-conorms, complement). Behaviours of such different classifiers are illustrated on artificially generated examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".