Stevo15025/FRaC: Anomaly Detection

FRaC is a general approach to the anomaly detection problem that is, the task of identifying instances that come from a different class or distribution than the majority (unsupervised anomaly detection) or a set of verified "normal" data (semi-supervised anomaly detection).The key to making this approach work is to precisely quantify the amount of evidence provided by each observation. To this end, FRaC developes a novel, information-theoretic anomaly measure that combines the contributions of all feature models.

install.packages("remotes")
remotes::install_github("Stevo15025/FRaC")