  Random Forests (RFs) are strong machine learning tools for classification and
regression. However, they remain supervised algorithms, and no extension of RFs
to the one-class setting has been proposed, except for techniques based on
second-class sampling. This work fills this gap by proposing a natural
methodology to extend standard splitting criteria to the one-class setting,
structurally generalizing RFs to one-class classification. An extensive
benchmark of seven state-of-the-art anomaly detection algorithms is also
presented. This empirically demonstrates the relevance of our approach.
