<title>New initial basic probability assignments for multiple classifiers</title>
Bibliographic record
Abstract
In the field of pattern recognition, more specifically in the area of supervised and feature-vector-based classifications, various classification methods exist but none of them can return always right results for any given kind of data. Each classifier behaves differently, having its own strengths and weaknesses. Some are more efficient then others in particular situations. Performances of these individual classifiers can be improved by combining them into one multiple classifier. In order to make more realistic decisions, the multiple classifier can analyze internal values generated by each classifier and can also rely on statistics learned from previous tests, such as reliability rates and confusion matrix. Individual classifiers studied in this project are Bayes, k-nearest neighbors, and neural network classifiers. They are combined using the Dempster-Shafer theory. The problem simplifies in finding weights that best represent individual classifier evidences. A particular approach has been developed for each of them, and for all of them it has been proven better to rely on classifiers internal information rather than statistics. When tested on a database comprised of 8 different kinds of military ships, represented by 11 features extracted from FLIR images, the resulting multiple classifier has given better results than others reported in the literature and tested in this work.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.043 |
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".