Lower bounds in classification for feature and algorithm selection
Bibliographic record
Abstract
The objective of this paper is to study recent advancements in estimation lower bound classification results. These lower bounds are estimated for a given set of features, targets, Signal to Noise Ratios (SNRs), and representative clutter environments. The motivation of this work comes from the desire to know the best achievable classification results for a given set of features at a range of SNRs and sensor data. This will assist the end user and classifier designer to select features that maximize the theoretical classification performance (i.e. minimize the classification errors in the confusion matrix tables). It will also assist in selecting the suitable classification algorithms approaching the lower theoretical classification bounds. The theoretical bounds used in this paper in our experimental examples are based on the Bayesian approach. However, other bounds are also reviewed. These results can be applied for selecting features and classifiers for earth and deep space observations and surveillance.
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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 | 0.023 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".