Abstract
This study introduces Active Down-sampling (ADS), a novel approach combining downsampling 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Advances in Artificial Intelligence and Machine Learning
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
