Unsupervised Extremely Randomized Trees

Kevin Dalleau 1 Miguel Couceiro 1 Malika Smaïl-Tabbone 1, 2
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper we present a method to compute dissimilarities on unlabeled data, based on extremely randomized trees. This method, Unsupervised Extremely Randomized Trees, is used jointly with a novel randomized labeling scheme we describe here, and that we call AddCl3. Unlike existing methods such as AddCl1 and AddCl2, no synthetic instances are generated, thus avoiding an increase in the size of the dataset. The empirical study of this method shows that Unsupervised Extremely Randomized Trees with AddCl3 provides competitive results regarding the quality of resulting clusterings, while clearly outperforming previous similar methods in terms of running time.
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Pré-publication, Document de travail
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Contributeur : Kevin Dalleau <>
Soumis le : mardi 19 décembre 2017 - 11:45:50
Dernière modification le : mardi 17 avril 2018 - 09:04:24


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  • HAL Id : hal-01667317, version 1


Kevin Dalleau, Miguel Couceiro, Malika Smaïl-Tabbone. Unsupervised Extremely Randomized Trees. 2017. 〈hal-01667317〉



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