Explainable Anomaly Detection
Explainable Anomaly Detection is an anomaly detection framework that, as a subclass of interpretable machine learning methods, provides human-understandable justifications for detected anomalies by revealing the underlying decision logic and feature contributions that distinguish it from opaque anomaly detection models.
145
Publications
7.3K
Citations
550
Authors
228
Institutions
Publications per year
2017–2026
137
Parents
145
A Unifying Review of Deep and Shallow Anomaly Detection
Proceedings of the IEEE · 2021 · 812 citations · Full text
BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series
Bin Zhou, Shenghua Liu, Bryan Hooi et al. · 2019 · 272 citations · Full text
1–5 of 145
550
4
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3
h-index
Indiana University – Purdue University Indianapolis, United States
3 publications · 155 citations
3
h-index
3
h-index
3
h-index
1–5 of 550
228
University of Kaiserslautern
Kaiserslautern, Germany
4 h-index
8 publications
382 citations
University of Canberra
Canberra, Australia
3 h-index
6 publications
1.1K citations
École Polytechnique
Palaiseau, France
3 h-index
14 publications
516 citations
Technische Universität Berlin
Berlin, Germany
3 h-index
9 publications
381 citations

UNSW Sydney
Sydney, Australia
3 h-index
6 publications
1.1K citations
Indiana University – Purdue University Indianapolis
Indianapolis, United States
3 h-index
9 publications
465 citations
University of Bari Aldo Moro
Bari, Italy
3 h-index
11 publications
467 citations
Leiden University
Leiden, The Netherlands
3 h-index
8 publications
432 citations
1–8 of 228
Repository · 13 publications · 398 citations
12
h-index
9 publications · 687 citations
9
h-index
3 publications · 143 citations
3
h-index
3 publications · 360 citations
3
h-index
TKDE · 2 publications · 164 citations
2
h-index
1–5