BR-MARLENE and BRPW-MARLENE use weighted ensembles of per-label and pairwise-label classifiers to transfer knowledge across labels and sources in non-stationary multi-label streams, outperforming state-of-the-art baselines.
Ddd: A new ensemble approach for dealing with concept drift,
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Multi-Label Transfer Learning in Non-Stationary Data Streams
BR-MARLENE and BRPW-MARLENE use weighted ensembles of per-label and pairwise-label classifiers to transfer knowledge across labels and sources in non-stationary multi-label streams, outperforming state-of-the-art baselines.