arXiv:2602. 22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression.
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arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.
arXiv:2608.30923v1 Announce Type: cross Abstract: Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learne...
arXiv:2606. 01221v1 Announce Type: cross Abstract: Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performance on rare but important cases.
The paper introduces Backward Kernel Herding, an algorithm that iteratively removes data points to create representative subsets for kernel learning, achieving performance comparable to state‑of‑the‑art methods while speeding up subsampling when the reduced size is less than half the original dataset. It also proposes Flexible Kernel Thinning, an extension that allows construction of subsets of any size, not just successive halvings, and demonstrates that this method often yields the best predictive performance. Experiments on Gaussian Processes and Kernel Support Vector Machines show that Backward Kernel Herding excels in training‑time efficiency, while Flexible Kernel Thinning offers superior predictive accuracy and competitive memory usage, emphasizing the need to choose a reduction strategy based on the desired trade‑off between performance, cost, and memory.
arXiv:2607. 17178v1 Announce Type: cross Abstract: Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution.
arXiv:2609.39215v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity....