Little data is often enough for distance-based outlier detection

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

8 Zitate (Scopus)

Abstract

Many real-world use cases benefit from fast training and prediction times, and much research went into speeding up distance-based outlier detection methods to millions of data points. Contrary to popular belief, our findings suggest that little data is often enough for distance-based outlier detection models. We show that using only a tiny fraction of the data to train distance-based outlier detection models often leads to no significant reduction in predictive performance and detection variance over a wide range of tabular datasets. Furthermore, we compare a data reduction based on random subsampling and clustering-based prototypes and show that both approaches yield similar outlier detection results. Simple random subsampling, thus, proves to be a useful benchmark and baseline for future research on speeding up distance-based outlier detection.

OriginalspracheEnglisch
Seiten (von - bis)984-992
Seitenumfang9
FachzeitschriftProcedia Computer Science
Jahrgang200
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Österreich
Dauer: 19 Nov. 202121 Nov. 2021

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