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.
How good is the Electricity benchmark for evaluating concept drift adaptation
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In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.
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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.