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. 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.