Anomaly Detection Techniques Based on Kappa-Pruned Ensembles
Bibliographic record
Abstract
Ensemble-based anomaly detection systems (ADSs), using Boolean combination, have been shown to reduce the false alarm rate over that of a single detector. However, the existing Boolean combination methods rely on an exponential number of combinations making them impractical, even for a small number of detectors. In this paper, we propose weighted pruning-based Boolean combination, an efficient approach for selecting and combining accurate and diverse anomaly detectors. It works in three phases. The first phase selects a subset of the available base diverse soft detectors by pruning all the redundant soft detectors based on a weighted version of Cohen's kappa measure of agreement. The second phase selects a subset of diverse and accurate crisp detectors from the base soft detectors (selected in Phase1) based on the unweighted kappa measure. The selected complementary crisp detectors are then combined in the final phase using Boolean combinations. The results on two large scale datasets show that the proposed weighted pruning approach is able to maintain and even improve the accuracy of existing Boolean combination techniques, while significantly reducing the combination time and the number of detectors selected for combination.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 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".