TY  - GEN
TI  - Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles.
EP  - 316
SP  - 301
AV  - public
Y1  - 2019/03/28/
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10095178
N2  - 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.
UR  - https://doi.org/10.1007/978-3-030-43823-4_26
PB  - Springer
CY  - Cham
KW  - Decentralized learning
KW  -  Classifier ensemble
KW  -  Copulas
A1  - Klein, J
A1  - Albardan, M
A1  - Guedj, B
A1  - Colot, O
T3  - Communications in Computer and Information Science
ER  -