Guest editorial: Non-IID outlier detection in complex contexts
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...
Saved in:
Main Authors: | PANG, Guansong, ANGIULLI, Fabrizio, CUCURINGU, Mihai, LIU, Huan |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2021
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7018 https://ink.library.smu.edu.sg/context/sis_research/article/8021/viewcontent/Guest_Editorial_Non_IID_Outlier_Detection_in_Complex_Contexts.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Homophily outlier detection in non-IID categorical data
by: PANG, Guansong, et al.
Published: (2021) -
Identifying outlier opinions in an online intelligent argumentation system
by: ARVAPALLY, R., et al.
Published: (2017) -
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection
by: PANG, Guansong, et al.
Published: (2017) -
Zero-shot out-of-distribution detection with outlier label exposure
by: DING, Choubo, et al.
Published: (2024) -
Heterogeneous univariate outlier ensembles in multidimensional data
by: PANG, Guansong, et al.
Published: (2020)