What Knowledge Transfers in Tabular Anomaly Detection? A Teacher–Student Distillation Analysis

Anomaly detection on tabular data is widely used in fraud detection, predictive maintenance, and medical screening. While heterogeneous ensembles combining multiple detection paradigms achieve strong performance, their computational cost limits deployment in latency-sensitive or resource-constrained...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Tea Krčmar, Dina Šabanović, Miljenko Švarcmajer, Ivica Lukić
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2026-03-01
Sarja:	Machine Learning and Knowledge Extraction
Aiheet:	anomaly detection knowledge distillation teacher–student learning ensemble methods tabular data temperature scheduling
Linkit:	https://www.mdpi.com/2504-4990/8/3/60
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