Efficient outlier detection in numerical and categorical data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract How to spot outliers in a large, unlabeled dataset with both numerical and categorical attributes? How to do it in a fast and scalable way? Outlier detection has many applications; it is covered therefore by an extensive literature. The distance-based detectors are the most popular ones. However, they still have two major drawbacks: (a) the intensive neighborhood search that takes hours or even days to complete in large data, and; (b) the inability to process categorical attributes. This paper tackles both problems by presenting HySortOD : a new, fast and scalable detector for numerical and categorical data. Our main focus is the analysis of datasets with many instances, and a low-to-moderate number of attributes. We studied dozens of real, benchmark datasets with up to one million instances ; HySortOD outperformed nine competitors from the state of the art in runtime, being up to six orders of magnitude faster in large data, while maintaining high accuracy. Finally, we also performed an extensive experimental evaluation that confirms the ability of our method to obtain high-quality results from both real and synthetic datasets with categorical attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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