An Adaptive and Diversity-Based Ensemble Method for Binary Classification
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, machine learning techniques have been rapidly developed and widely applied to many industrial and academic fields. Moreover, as an important part of machine learning, ensemble techniques have drawn significant attention in both academic researches and practical applications, which make use of multiple single models to construct a hybrid model. Usually, compared to each individual model, a better performance can be achieved by ensemble methods. In this thesis, a novel ensemble method is proposed to improve the performance for binary classification. The proposed method can non-linearly combine the base models by adaptively selecting the most suitable one for each data instance. The new approach has been validated on two datasets, and the experiments results show an up to 18.5% improvement on F1 score compared to the best individual model. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in improving F1 score.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it