A reliable composite classification strategy
Bibliographic record
Abstract
A composite classification scheme is proposed by combining several classifiers with distinctly different design methodologies. The classifiers are selected from a number of state of the art pattern classification schemes with a view to obtain superior performance. In this scheme, no a priori information except a set of pre-classified data is assumed to be available. By using distinctly different classifiers, the common mode data misclassification is reduced. Traditionally, after the design and evaluation phase, the pre-classified data is discarded. In this scheme, however, the misclassified data from each classifier in the training set is tagged and stored with a view to weight the decisions of the classifiers. If a given test sample is close to a misclassified data cluster of a particular classifier, then the decision made by this classifier is given a lower weighting. The final decision is made by analysing the weighted combination of individual classifier decisions. The proposed algorithm is evaluated on both simulated data and on a biological cell classification problem and it is shown that improved accuracy is obtained when compared to that of the most accurate classifier.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".