arXiv AI
Towards Stream Learning on Embedded Systems: Benchmarking the Memory Consumption of Stream Learning Methods
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arXiv Machine Learning
Aug 5
NOMADD: Numerical Optimization of Models Adapting to Data Drift
arXiv:2608. 02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively.
arXiv Machine Learning
5d ago
Inference-Time Prior Adaptation for Imbalanced Data Streams under Concept Drift
The paper introduces PAI-KNN, a k‑nearest neighbor classifier designed for data stream classification with concept drift and class imbalance. Unlike traditional methods that modify memory through instance removal or resampling, PAI‑KNN keeps a simple sliding‑window buffer and adapts its decision rule at inference time by adjusting class priors based on recent observations. Experiments on synthetic streams with varying imbalance and drift show that PAI‑KNN performs competitively with state‑of‑the‑art techniques, especially when dynamic class imbalance and concept drift are present.
arXiv Machine Learning
Aug 21
When to Retrain: An Empirical Study of Retraining Policies for Streaming ML Under Concept Drift, Budget, and Latency Constraints
arXiv:2608. 19488v1 Announce Type: new Abstract: Production machine learning systems degrade under concept drift, yet practitioners have little principled guidance on when to retrain.
arXiv Machine Learning
Jun 9
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
arXiv AI
Aug 18
Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.
arXiv AI
Jun 26
Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.