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One Class Splitting Criteria for Random Forests

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.
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Preprints, Working Papers, ...
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Contributor : Nicolas Goix Connect in order to contact the contributor
Submitted on : Friday, November 18, 2016 - 1:54:42 PM
Last modification on : Wednesday, November 3, 2021 - 10:00:21 AM
Long-term archiving on: : Monday, March 20, 2017 - 11:45:00 PM


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  • HAL Id : hal-01392563, version 2
  • ARXIV : 1611.01971



Nicolas Goix, Nicolas Drougard, Romain Brault, Maël Chiapino. One Class Splitting Criteria for Random Forests. 2016. ⟨hal-01392563v2⟩



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