A Comparative Study of Cost-Sensitive Classifiers
Why this work is in the frame
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Bibliographic record
Abstract
The authors briefly review the theory of cost-sensitive learning, and the exist ing cost-sensitive learning algorithms. The authors categorize cost-sensitive learning algorithms into direct cost-sensitive learning and cost-sensitive met a-learning, which converts cost-insensitive classifiers into cost-sensitive o nes. The authors also propose a simple yet general and effective meta-learning method called Empirical Threshold Adjusting (ETA for short). The authors evalu ate the performance of various cost-sensitive meta-learning algorithms includi ng ETA. ETA almost always produces the lowest misclassification cost, and is l east sensitive to the misclassification cost ratio. Other useful conclusions on cost-sensitive meta-learning methods are drawn. This is an improved and expanded version of the paper Thresholding for Maki ng Classifiers Cost-sensitive by Victor S.Sheng and Charles X.Ling, publis hed in AAAI 2006.
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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 | 0.001 | 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