MARLINE uses centroid-based affine projections to let classifiers from non-matching source streams vote on target predictions under concept drift, outperforming several state-of-the-art methods in synthetic and real-world benchmarks.
Estimation of the box correction for degrees of freedom from sample data in randomized block and split- plot designs,
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MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
MARLINE uses centroid-based affine projections to let classifiers from non-matching source streams vote on target predictions under concept drift, outperforming several state-of-the-art methods in synthetic and real-world benchmarks.