Outlier detection (also known as anomaly detection) aims at identifying data objects which are rare or significantly different from the majority of objects. Due to the significance to 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 is Independent and Identically Distributed (IID), which are significantly challenged in complex contexts where data is actually non-IID. These contexts are ubiquitous in not only graph data, sequence data, spatial data and time series data but also traditional multidimensional, textual and image data. This demands advanced outlier detection approaches to well address those explicit or implicit non-IID data characteristics. This special issue on Non-IID Outlier Detection in Complex Contexts will solicit the latest advancements in outlier detection that consider the data interactions, relations, and heterogeneity to enable a more effective identification of the outliers and to provide more reliable outlier detection systems in the aforementioned critical domains.
This special issue solicits original and high-quality research on but not limited to the following topics:
All submissions must comply with the submission guidelines of IEEE Intelligent Systems and will be reviewed by research peers.
• Dr. Guansong Pang (University of Adelaide, Australia; guansong.pang@adelaide.edu.au)
• Prof. Fabrizio Angiulli (University of Calabria, Italy; fabrizio.angiulli@unical.it)
• Prof. Mihai Cucuringu (University of Oxford, United Kingdom; mihai.cucuringu@stats.ox.ac.uk)
• Prof. Huan Liu (Arizona State University, United States; huan.liu@asu.edu)
Inquiries can be sent to the Guest Editors at is6-20@computer.org
