Titre :

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

Auteur(s) :

Klein, John [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Albardan, Mahmoud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Guedj, Benjamin [Auteur]
Department of Computer science [University College of London] [UCL-CS]
University College of London [London] [UCL]
Inria-CWI [Inria-CWI]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Colot, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

ECML-PKDD, Decentralized Machine Learning at the Edge Workshop

Ville :

Wurzburg

Pays :

Allemagne

Date de début de la manifestation scientifique :

2019-09-16

Mot(s)-clé(s) en anglais :

classifers ensemble
machine learning
copulas
Decentralized learning
classifier ensemble

Discipline(s) HAL :

Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

We examine a network of learners which address the same classification task but must learn from different data sets. The learners can share a limited portion of their data sets so as to preserve the network load. We introduce ...
Lire la suite >We examine a network of learners which address the same classification task but must learn from different data sets. The learners can share a limited portion of their data sets so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach in which the shared data and the trained models are sent to a central machine that allows to build an ensemble of classifiers. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness as compared to gold standard approaches. A companion python implementation can be downloaded at https://github.com/john-klein/DELCOLire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL