Static and dynamic overproduction and selection of classifier ensembles with genetic algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Le sujet de cette these porte sur la selection statique et dynamique des ensembles de classificateurs avec algorithmes genetiques. La premiere contribution concerne le travail experimental realise pour caracteriser la diversite dans les ensembles de classificateurs (selection statique). Un cadre experimental tres rigoureux a permis de confirmer empiriquement que la diversite utilisee comme fonction objectif ne permet pas un choix optimal du meilleur ensemble. Une deuxieme contribution importante concerne le phenomene du surapprentissage cause par les algorithmes genetiques mono et multi critere utilises pour la selection des classificateurs. Trois strategies ont ete analysees pour tenter de controler le surapprentissage. Finalement, la contribution la plus importante de cette these est la mise en oeuvre d'une nouvelle strategie pour la selection dynamique des ensembles de classificateurs. L'approche proposee permet la selection, pour chaque exemple a classer, du sous-ensemble de classificateurs le plus confiant pour decider de la classe d'appartenance.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it