Data Cleaning for Unsupervised Anomaly Detection
Bibliographic record
Abstract
Training data sets for unsupervised anomaly detection, expected to be anomaly-free, often contain anomalies (i.e., contamination) which can significantly degrade model performance. Most contamination-robust anomaly detection methods do not generalize across different architectures and rely on contamination ratio information which may not be available. In this paper, we propose a novel self-contained preprocessing anomaly detection approach focusing on data cleaning, i.e., identification and treatment of contamination within a training data set for a more effective unsupervised anomaly detection, all without relying on contamination ratio. Our approach is based on dynamic analysis of the cosine similarity between each data point and its reconstruction during training of an auto-encoder, anticipating low density in most anomaly regions within the dynamic space. This approach enhances data quality through hard cleaning: by removing all identified anomalous candidates from training data. Data cleaning allows generalization across various architectures as the improvements made at the data level benefit any anomaly detection model. This process yields up to 100% of contamination reduction, resulting average improvements of 11.2% in F1 score and 7.3% in accuracy across 8 models and 10 benchmark data sets.
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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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.006 |
| 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".