Bibliographic record
Abstract
Abstract Anomaly detection in streaming data is an important problem in numerous application domains. Most existing model‐based approaches to stream learning are based on decision trees due to their fast construction speed. This paper introduces streaming autoencoder (SA), a fast and novel anomaly detection algorithm based on ensembles of neural networks for evolving data streams. It is a one‐class learner, which only requires data from the positive class for training and is accurate even when anomalous training data are rare. It features an ensemble of threaded autoencoders with continuous learning capacity. Furthermore, the SA uses a 2‐step detection mechanism to ensure that real anomalies are detected with low false‐positive rates. The method is highly efficient because it processes data streams in parallel with multithreads and alternating buffers. Our analysis shows that SA has a linear runtime and requires constant memory space. Empirical comparisons to the state‐of‐the‐art methods on multiple benchmark data sets demonstrate that the proposed method detects anomalies efficiently with fewer false alarms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".