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One Class Splitting Criteria for Random Forests

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).

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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:
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Deposited By: Nicolas Drougard
Deposited On:13 Dec 2017 09:35

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