A New Random Forest Method for One-Class Classification

  • Chesner Désir
  • Simon Bernard
  • Caroline Petitjean
  • Laurent Heutte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


We propose a new one-class classification method, called One Class Random Forest, that is able to learn from one class of samples only. This method, based on a random forest algorithm and an original outlier generation procedure, makes use of the ensemble learning mechanisms offered by random forest algorithms to reduce both the number of artificial outliers to generate and the size of the feature space in which they are generated. We show that One Class Random Forests perform well on various UCI public datasets in comparison to few other state-of-the-art one class classification methods (gaussian density models, Parzen estimators, gaussian mixture models and one-class SVMs).


One-class classification decision trees ensemble methods random forests outlier generation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chesner Désir
    • 1
  • Simon Bernard
    • 2
  • Caroline Petitjean
    • 1
  • Laurent Heutte
    • 1
  1. 1.Université de Rouen, LITIS EA 4108Saint-Etienne-du-RouvrayFrance
  2. 2.Department of EECS et GIGA-ResearchUniversité de LiègeLiègeBelgium

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