A Meta-Analysis of the Anomaly Detection Problem

By Andrew Emmott, Shubhomoy Das, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong. 2015

96 citations recorded by the source corpus.

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In Anomaly & Outlier Detection

Citations in the corpus

Pretrained Transformers Improve Out-of-Distribution Robustness (2020, background): “Accordingly, models must generalize to OOD examples whenever possible, and when OOD examples do not belong to any known class, models must detect them in order to abstain or trigger a conservative fallback policy (Emmott et al., 2015).”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “, in fraud and cybersecurity scenarios [204], [347], [430]–[433]).”
Extended Isolation Forest (2018, background): “For a survey in research in anomaly detection see [3], [6].”
Scaling Out-of-Distribution Detection for Real-World Settings (2022, background): “Accordingly, research on out-of-distribution detection has a rich history spanning several decades (Schölkopf et al., 1999; Breunig et al., 2000; Emmott et al., 2015).”
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection (2019, methodology): “In this section, we provide a comprehensive empirical analysis of NN-methods by comparing kNN, kNN, and DTM2(1) to IForest, LOF and LODA on (1) the corpus of synthetic datasets developed in [24], (2) 23 real datasets from the ODDS library [25], and (3) 6 high dimensional datasets from the UCI librar”
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, methodology): “Notably, iForest has been the SOTA method in many large-scale outlier analysis [54], [61].”
ADBench: Anomaly Detection Benchmark (2022, methodology): “Besides, the change of anomaly frequency would also affect the behavior of detection methods [42].”