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ocrf.html
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<?xml version="1.0" encoding="UTF-8"?>
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<title>Nicolas Goix</title>
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<div class="menu-category">Nicolas Goix</div>
<div class="menu-item"><a href="index.html">Home</a></div>
<div class="menu-item"><a href="biography.html">Biography</a></div>
<div class="menu-category">Research</div>
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<div class="menu-item"><a href="sklearn.html">Scikit-Learn</a></div>
<div class="menu-item"><a href="damex.html">Damex Algorithm</a></div>
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<div class="menu-item"><a href="nyu.html">Black hole Cyg-X-1</a></div>
<div class="menu-item"><a href="ocrf.html" class="current">One Class Random Forests</a></div>
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<h1>Nicolas Goix – One Class Random Forests</h1>
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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. Our <a href="https://arxiv.org/abs/1611.01971" target="_blank"> paper</a> 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.
The associated code uses an adapted version of scikit-learn cython code for trees and is available <a href="https://github.com/ngoix/OCRF" target="_blank"> here</a>.
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