Classification with Margin Constraints: A Unification with Applications to Optimization
Bibliographic record
Abstract
This paper introduces Classification with Margin Constraints (CMC), a simple generalization of cost-sensitive classification that unifies several learning settings. In particular, we show that a CMC classifier can be used, out of the box, to solve regression, quantile estimation, and several anomaly detection formulations. On the one hand, our reductions to CMC are at the loss level: the optimization problem to solve under the equivalent CMC setting is exactly the same as the optimization problem under the original (e.g. regression) setting. On the other hand, due to the close relationship between CMC and standard binary classification, the ideas proposed for efficient optimization in binary classification naturally extend to CMC. As such, any improvement in CMC optimization immediately transfers to the domains reduced to CMC, without the need for new derivations or programs. To our knowledge, this unified view has been overlooked by the existing practice in the literature, where an optimization technique (such as SMO or PEGASOS) is first developed for binary classification and then extended to other problem domains on a case-by-case basis. We demonstrate the flexibility of CMC by reducing two recent anomaly detection and quantile learning methods to CMC.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".