Abstract

This study introduces Active Down-sampling (ADS), a novel approach combining down-sampling with active learning to select informative samples from the majority class in imbalanced data scenarios, thereby enhancing machine learning model performance. Tested on three real-world datasets (BLOOD, Yeast, and Ecoli), ADS demonstrates superior classification accuracy over existing methods, efficiently balancing dataset representation while saving computational resources. It boosts accuracy across both minority and majority classes, optimizes resource use, and reduces misclassification costs. It emerges as a promising solution to the prevalent issue of data imbalance in machine learning, offering significant performance, resource, and cost advantages.

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