Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. 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. The demand for advanced outlier-detection approaches to address those explicit or implicit non-IID data characteristics. Motivated by this demand, researchers organized a Special Issue in IEEE Intelligent Systems to solicit the latest advancements in this topic in October 2019.
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence