Name
Title | ||
|---|---|---|
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles. |
Abstract | ||
|---|---|---|
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 this https URL |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-43823-4_26 | arXiv: Machine Learning |
Field | DocType | Volume |
Data set,Copula (linguistics),Robustness (computer science),Gaussian,Artificial intelligence,Statistical model,Classifier (linguistics),Ensemble learning,Mathematics,Python (programming language),Machine learning | Journal | abs/1804.10028 |
ISSN | Citations | PageRank |
ECML-PKDD 2019 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Klein | 1 | 77 | 10.14 |
| Mahmoud Albardan | 2 | 0 | 0.34 |
| Benjamin Guedj | 3 | 9 | 8.82 |
| Olivier Colot | 4 | 129 | 15.55 |