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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
Opus teacher head0.155
GPT teacher head0.390
- Teacher spread
- 0.234 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- Data Mining and Knowledge Discovery
- Topic
- Anomaly Detection Techniques and Applications
- Field
- Computer Science
- Canadian institutions
- University of Alberta
- Funders
- Conselho Nacional de Desenvolvimento Científico e TecnológicoCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaFundação de Amparo à Pesquisa do Estado de São PauloTeknologi og Produktion, Det Frie Forskningsråd
- Keywords
- OutlierAnomaly detectionBenchmark (surveying)Computer scienceData miningArtificial intelligenceGround truthPattern recognition (psychology)Set (abstract data type)Machine learning
- Has abstract in OpenAlex
- no