Histogram approaches for imbalanced data streams regression

• URL: http://arxiv.org/abs/2501.17568v1
• Date: Wed, 29 Jan 2025 11:03:02 GMT
• Title: Histogram approaches for imbalanced data streams regression
• Authors: Ehsan Aminian, Joao Gama, Rita P. Ribeiro,
• Abstract summary: We introduce novel data-level sampling strategies, textttHistUS and textttHistOS, that utilize histogram-based approaches to balance data streams. We demonstrate that textttHistUS and textttHistOS outperform traditional methods through extensive experiments on synthetic and real-world datasets.
• Score: 1.8385275253826225
• License:
• Abstract: Handling imbalanced data streams in regression tasks presents a significant challenge, as rare instances can appear anywhere in the target distribution rather than being confined to its extreme values. In this paper, we introduce novel data-level sampling strategies, \texttt{HistUS} and \texttt{HistOS}, that utilize histogram-based approaches to dynamically balance data streams. Unlike previous methods based on Chebyshev\textquotesingle s inequality, our proposed techniques identify and handle rare cases across the entire distribution effectively. We demonstrate that \texttt{HistUS} and \texttt{HistOS} outperform traditional methods through extensive experiments on synthetic and real-world datasets, leading to more accurate and robust regression models in streaming environments.

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