Histogram approaches for imbalanced data streams regression

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, HistUS and HistOS, that utilize histogram-based approaches to dynamically balance data streams. Unlike previous methods based on Chebyshev s inequality, our proposed techniques identify and handle rare cases across the entire distribution effectively. We demonstrate that HistUS and HistOS outperform traditional methods through extensive experiments on synthetic and real-world datasets, leading to more accurate and robust regression models in streaming environments.

Tasks

Reproductions