One Class Splitting Criteria for Random Forests

Nicolas Goix (1) , Nicolas Drougard (2) , Romain Brault (1) , Maël Chiapino (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace

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.

Domaines: Machine Learning [stat.ML]

Nicolas Goix, Nicolas Drougard, Romain Brault, Maël Chiapino. One Class Splitting Criteria for Random Forests. 2016. ⟨hal-01392563v1⟩