Goix, Nicolas and Drougard, Nicolas and Brault, Romain and Chiapino, Mael
One Class Splitting Criteria for Random Forests.
(2017)
In: The 9th Asian Conference on Machine Learning, 15 November 2017 - 17 November 2017 (Seoul, Korea, Republic Of).
|
(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 603kB |
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.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
HAL Id: | hal-01662421 |
Audience (journal): | International peer-reviewed journal |
Audience (conference): | International conference proceedings |
Uncontrolled Keywords: | |
Institution: | Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE) Other partners > Telecom ParisTech (FRANCE) |
Laboratory name: | |
Statistics: | download |
Deposited On: | 13 Dec 2017 09:35 |
Repository Staff Only: item control page