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Your search keyword '"Bouscarrat, Léo"' showing total 3 results
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3 results on '"Bouscarrat, Léo"'

2. Pruning Random Forest with Orthogonal Matching Trees

Author

Giffon, Luc, Lamothe, Charly, Bouscarrat, Léo, Milanesi, Paolo, Cherfaoui, Farah, Koço, Sokol, éQuipe d'AppRentissage de MArseille (QARMA), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0026,LIVES,Apprendre avec des vues interactionnelles(2015), ANR-16-CE23-0006,Deep_in_France,Réseaux de neurones profonds pour l'apprentissage(2016), European Project: 788240,COVOPRIM, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Cherfaoui, Farah, Apprendre avec des vues interactionnelles - - LIVES2015 - ANR-15-CE23-0026 - AAPG2015 - VALID, Réseaux de neurones profonds pour l'apprentissage - - Deep_in_France2016 - ANR-16-CE23-0006 - AAPG2016 - VALID, and A Comparative Study of Voice Perception in Primates - COVOPRIM - 788240 - INCOMING

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], pruning, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], OMP, random forest
Abstract

National audience; In this paper we propose a new method to reduce the size of Breiman's Random Forests. Given a Random Forest and a target size, our algorithm builds a linear combination of trees which minimizes the training error. Selected trees, as well as weights of the linear combination are obtained by mean of the Orthogonal Matching Pursuit algorithm. We test our method on many public benchmark datasets both on regression and binary classification and we compare it to other pruning techniques. Experiments show that our technique performs significantly better or equally good on many datasets 1. We also discuss the benefit and shortcoming of learning weights for the pruned forest which lead us to propose to use a non-negative constraint on the OMP weights for better empirical results.

Published
2020

3. Multilingual enrichment of disease biomedical ontologies

Author

Bouscarrat, Léo, Bonnefoy, Antoine, Capponi, Cécile, Ramisch, Carlos, éQuipe d'AppRentissage de MArseille (QARMA), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Traitement Automatique du Langage Ecrit et Parlé (TALEP), and EURA NOVA

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, [INFO.INFO-WB]Computer Science [cs]/Web, translation, biomedical, Quantitative Biology - Quantitative Methods, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science - Information Retrieval, FOS: Biological sciences, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ontology, wikidata, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Computation and Language (cs.CL), Quantitative Methods (q-bio.QM), Information Retrieval (cs.IR)
Abstract

International audience; Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both ontologies, plus Arabic, Chinese and Russian for the second one. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.

Published
2020

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