Statistical Tests to Identify Virtual Concept Drifts
Why this work is in the frame
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Bibliographic record
Abstract
In streaming environments, concept drift is a common problem and identifying whether it is occurring is of utmost importance. Most of the published drift detection methods work based on the results of a base classifier, for example, by using the classification error or the distance between two consecutive errors. But if a change occurs only in the attributes space without changing the boundaries inferred by the learner, drift detection methods may not able to correctly work. This paper proposes VDDM, a drift detection method specially able to identify virtual concept drifts. It works by using a multivariate nonparametric statistical test to identify changes in a window of the most recent instances. Experimental results indicate that the usage of a multivariate nonparametric statistical test presents competitive results specially in the number of detected changes, distances to the drift point, sensitivity and specificity scores, as well as the Matthews Correlation Coefficient and the F1 score.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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