One Class Splitting Criteria for Random Forests
Machine Learning
2016-11-22 v3 Machine Learning
Abstract
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.
Cite
@article{arxiv.1611.01971,
title = {One Class Splitting Criteria for Random Forests},
author = {Nicolas Goix and Nicolas Drougard and Romain Brault and Maël Chiapino},
journal= {arXiv preprint arXiv:1611.01971},
year = {2016}
}