Accumulated Relative Density Outlier Detection For Large Scale Traffic Data
Bibliographic record
Abstract
Outlier detection (OD) has been popularly developed in many fields such as medical diagnosis, network intrusion detection, fraud detection and military surveillance. This paper presents an accumulated relative density (ARD) OD method to identify outliers which possess relatively low or high local density. Previously, many density-based OD methods, such as local outlier factor (LOF) and Local Correlation Integral (LOCI), are applied to detect outliers which have low relative density in the data set. Relative local density (RLD) is measured and then compared with each other by statistics to label abnormities. In the proposed ARD method, a big circle centered at every data point is formed first. This big circle covers some data points with its radius. Then, for each encapsulated point inside this big circle, a small circle centered at itself is defined. Afterward, the ratio of number of covered data points inside the small circle of that particular point to the average number of data points in all small circles is defined as the RLD. After RLDs of all data points are calculated, a point whose RLD deviates greatly from the mean of all RLDs will be labeled as an outlier, otherwise as inliers. This ARD method was evaluated by a real world traffic data set which was originally represented as spatial-temporal (ST) traffic flow signals. The ST signals were processed by a principal component analysis (PCA) to reduce its dimension into two-dimensional 2D data points. An average 95% detection success rate (DSR) of OD can be achieved by this method.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".