On dynamic ensemble selection and data preprocessing for multi-class\n imbalance learning
Bibliographic record
Abstract
Class-imbalance refers to classification problems in which many more\ninstances are available for certain classes than for others. Such imbalanced\ndatasets require special attention because traditional classifiers generally\nfavor the majority class which has a large number of instances. Ensemble of\nclassifiers have been reported to yield promising results. However, the\nmajority of ensemble methods applied too imbalanced learning are static ones.\nMoreover, they only deal with binary imbalanced problems. Hence, this paper\npresents an empirical analysis of dynamic selection techniques and data\npreprocessing methods for dealing with multi-class imbalanced problems. We\nconsidered five variations of preprocessing methods and four dynamic selection\nmethods. Our experiments conducted on 26 multi-class imbalanced problems show\nthat the dynamic ensemble improves the F-measure and the G-mean as compared to\nthe static ensemble. Moreover, data preprocessing plays an important role in\nsuch cases.\n
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".