One Class Splitting Criteria for Random Forests

Nicolas Goix, Nicolas Drougard, Romain Brault, Mael Chiapino
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:343-358, 2017.

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 this Paper

BibTeX
@InProceedings{pmlr-v77-goix17a, title = {One Class Splitting Criteria for Random Forests}, author = {Goix, Nicolas and Drougard, Nicolas and Brault, Romain and Chiapino, Mael}, booktitle = {Proceedings of the Ninth Asian Conference on Machine Learning}, pages = {343--358}, year = {2017}, editor = {Zhang, Min-Ling and Noh, Yung-Kyun}, volume = {77}, series = {Proceedings of Machine Learning Research}, address = {Yonsei University, Seoul, Republic of Korea}, month = {15--17 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v77/goix17a/goix17a.pdf}, url = {https://proceedings.mlr.press/v77/goix17a.html}, 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.} }
Endnote
%0 Conference Paper %T One Class Splitting Criteria for Random Forests %A Nicolas Goix %A Nicolas Drougard %A Romain Brault %A Mael Chiapino %B Proceedings of the Ninth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Min-Ling Zhang %E Yung-Kyun Noh %F pmlr-v77-goix17a %I PMLR %P 343--358 %U https://proceedings.mlr.press/v77/goix17a.html %V 77 %X 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.
APA
Goix, N., Drougard, N., Brault, R. & Chiapino, M.. (2017). One Class Splitting Criteria for Random Forests. Proceedings of the Ninth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 77:343-358 Available from https://proceedings.mlr.press/v77/goix17a.html.

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