A drift-management framework that selects training segments by concept-drift scores and ranks batches inside them by random-forest leaf proximity to test data, yielding small accuracy gains over Quilt on most benchmark datasets.
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A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation
A drift-management framework that selects training segments by concept-drift scores and ranks batches inside them by random-forest leaf proximity to test data, yielding small accuracy gains over Quilt on most benchmark datasets.