Auto-calibration of Support Vector Machines for detecting disease outbreaks
Bibliographic record
Abstract
Support Vector Machines (SVM) have several tuning parameters such as the kernel function type. This work proposes to develop an algorithm to calibrate the SVM automatically for detecting disease outbreaks based on Telehealth data. Two sets of simulated data are generated based on real Telehealth calls and an outbreak profile. The Telehealth data is related to respiratory disease syndrome. The outbreak profile is created based on real outbreak data. The first data set is used by the SVM to model the relation between call counts and the occurrence of a respiratory outbreak; however, the other data set is used for testing the resulting model. This model is auto-calibrated by optimizing four parameters using a Genetic Algorithm. These parameters are the tradeoff between the training error and the margin of the classifying hyperplane, kernel function type used, the hyperplane type used and the threshold level at which the occurrence of an outbreak is detected.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".