RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for\n imbalanced data classification
Bibliographic record
Abstract
Real-world classification domains, such as medicine, health and safety, and\nfinance, often exhibit imbalanced class priors and have asynchronous\nmisclassification costs. In such cases, the classification model must achieve a\nhigh recall without significantly impacting precision. Resampling the training\ndata is the standard approach to improving classification performance on\nimbalanced binary data. However, the state-of-the-art methods ignore the local\njoint distribution of the data or correct it as a post-processing step. This\ncan causes sub-optimal shifts in the training distribution, particularly when\nthe target data distribution is complex. In this paper, we propose Radial-Based\nCombined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class\npotential to refine the energy-based resampling approach of CCR. In particular,\nRB-CCR exploits the class potential to accurately locate sub-regions of the\ndata-space for synthetic oversampling. The category sub-region for oversampling\ncan be specified as an input parameter to meet domain-specific needs or be\nautomatically selected via cross-validation. Our $5\\times2$ cross-validated\nresults on 57 benchmark binary datasets with 9 classifiers show that RB-CCR\nachieves a better precision-recall trade-off than CCR and generally\nout-performs the state-of-the-art resampling methods in terms of AUC and\nG-mean.\n
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".