An extended-shadow-code based approach for off-line signature verification. II. Evaluation of several multi-classifier combination strategies
Bibliographic record
Abstract
For pt.I see Proc. 12th ICPR, p.450-3. In a real situation, the choice of the best representation R(/spl gamma/) for the implementation of a signature verification system able to cope with all types of handwriting is a very difficult task. This study is original in that the design of the integrated classifiers E(x) is based on a large number of individual classifiers e/sub k/(x) (or signature representations R(/spl gamma/)) in an attempt to overcome in some way the need for feature selection. In this paper, the authors present a first systematical evaluation of a multi-classifier-based approach for off-line signature verification. Two types of integrated classifiers based on kNN or minimum distance classifiers and 15 types of representation related to the ESC used as a shape factor have been evaluated using a signature database of 800 images (20 writers/spl times/40 signatures per writer) in the context of random forgeries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".