Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2021

Abstract

Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. Due to its significance in many critical domains like cybersecurity, fintech, healthcare, public security, and AI safety, outlier detection has been one of the most active research areas in various communities, such as machine learning, data mining, computer vision, and statistics. Traditional outlier-detection techniques generally assume that data are independent and identically distributed (IID), which are significantly challenged in complex contexts where data are actually non-IID. These contexts are ubiquitous in not only graph data, sequence data, spatial data, temporal data, and streaming data, but also traditional multidimensional, textual, and image data.4–6 This demands for advanced outlierdetection approaches to address those explicit or implicit non-IID data characteristics.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Intelligent Systems

Volume

36

Issue

3

First Page

3

Last Page

4

ISSN

1541-1672

Identifier

10.1109/MIS.2021.3072704

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9470961

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