Learning aggregation for combining classifier ensembles
Bibliographic record
Abstract
Creating classifier ensembles and combining their outputs to achieve higher accuracy have been of recent interest. It was noted that when using such multiple classifier approaches the members of the ensemble should be error-independent. The ideal, in terms of ensembles of classifiers, would be a set of classifiers which do not show any coincident errors. That is, each of the classifiers generalized well, and when they did make errors on the test set, these errors were not shared with any other classifier. Various approaches for achieving this have been presented. This paper compares two approaches introduced for training multiple classifiers systems. These approaches are based on the feature based aggregation architecture and the adaptive training algorithm. An empirical evaluation using two data sets shows a reduction in the number of training cycles when applying the algorithm on the overall architecture, while maintaining the same or improved performance. The performance of these approaches is also compared to standard approaches proposed in the literature. The results substantiate the use of adaptive a-dining for both the ensemble and the aggregation architecture.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".