One class random forests

作者：

Highlights：

• We study the behavior of a new method for one-class classification, called One Class Random Forest (OCRF), that we have recently proposed.

• The OCRF method is based on a random forest algorithm and an original outlier generation procedure.

• OCRF is shown to perform well on various UCI public datasets when compared to state of the art one class methods, and performances are shown to be stable in higher dimension.

摘要

Highlights•We study the behavior of a new method for one-class classification, called One Class Random Forest (OCRF), that we have recently proposed.•The OCRF method is based on a random forest algorithm and an original outlier generation procedure.•OCRF is shown to perform well on various UCI public datasets when compared to state of the art one class methods, and performances are shown to be stable in higher dimension.

论文关键词：One class classification,Supervised learning,Decision trees,Ensemble methods,Random forests,Outlier generation,Outlier detection

论文评审过程：Received 11 October 2012, Revised 29 April 2013, Accepted 25 May 2013, Available online 8 June 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.05.022