Meta features for anomaly detection in AutoML

This abstract paper sheds light on automated machine learning systems used for anomaly detection. Such systems can propose optimal model for a given tasks by using meta learning as a core component. Systems presented in this paper are based on domain-specific meta features for choosing an optimal unsupervised model for anomaly detection. It also proposes further improvements by using multi-dimensional dense vectors to limit the dimensions of meta features.