Augmenting cost-SVM with gaussian mixture models for imbalanced classification
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".