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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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Submitted on : Wednesday, December 13, 2017 - 10:36:40 AM
Last modification on : Friday, October 9, 2020 - 9:44:45 AM


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  • HAL Id : hal-01662421, version 1
  • OATAO : 19281



Nicolas Goix, Nicolas Drougard, Romain Brault, Mael Chiapino. One Class Splitting Criteria for Random Forests. The 9th Asian Conference on Machine Learning, Nov 2017, Seoul, South Korea. pp. 1-16. ⟨hal-01662421⟩



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