Augmenting cost-SVM with gaussian mixture models for imbalanced classification
Why this work is in the frame
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Bibliographic record
Abstract
The Support Vector Machine (SVM), a known discriminative classifier is ineffective in dealing with imbalanced classificationproblems where the training examples of target class are outnumbered by non-target class examples. Though cost-SVM (cSVM)has been proposed to tackle the imbalanced datasets by assigning different cost functions to different classes, the performanceis less than satisfactory due to its limited ability to enforce cost-sensitivity. In this research, a generative classifier, GaussianMixture Model (GMM) is studied which can learn the distribution of the imbalanced data to improve the discriminative powerbetween imbalanced classes. By fusing this knowledge into cSVM, a model fusion approach, termed CSG (cSVM+GMM), isproposed to tackle the imbalanced classification problem. Experimental results on eleven benchmark datasets and one medicalimaging dataset show the effectiveness of CSG in dealing with imbalanced classification problems.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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