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.
DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broader stable-state/reactive-state process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.