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

Authors
  • Klein, John
  • Albardan, Mahmoud
  • Guedj, Benjamin
  • Colot, Olivier
Type
Preprint
Publication Date
Jul 15, 2019
Submission Date
Apr 26, 2018
Identifiers
DOI: 10.1007/978-3-030-43823-4_26
Source
arXiv
License
Yellow
External links

Abstract

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. 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 in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELCO

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