Klein, J;
Albardan, M;
Guedj, B;
Colot, O;
(2019)
Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles.
In: Cellier, P and Driessens, K, (eds.)
Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science.
(pp. pp. 301-316).
Springer: Cham.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles. |
| Event: | Machine Learning and Knowledge Discovery in Databases International Workshops of ECML PKDD 2019 |
| ISBN-13: | 978-3-030-43822-7 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-43823-4_26 |
| Publisher version: | https://doi.org/10.1007/978-3-030-43823-4_26 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Decentralized learning, Classifier ensemble, Copulas |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095178 |
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