KNNOR-Reg: A python package for oversampling in imbalanced regression
Samir Brahim Belhaouari, Ashhadul Islam, Khelil Kassoul, Ala Al‐Fuqaha, Abdesselam Bouzerdoum · Software Impacts · 2025
KNNOR-Reg is a Python package designed to address the challenge of imbalanced regression. While popular Python packages exist for tackling imbalanced classification, support for imbalanced regression remains limited. Imbalanced regression involves the underrepresentation of important ranges within a continuous target variable. KNNOR-Reg implements an oversampling technique that generates synthetic samples through interpolation between minority class samples and their nearest neighbors. The labels for synthetic samples are computed based on the inverse distance-weighted average of the nearest neighbors’ labels. KNNOR-Reg offers a user-friendly and extensible Python implementation for oversampling imbalanced regression data, aiming to reduce regressor bias and enhance model outcomes. • Novel Histogram-Based Oversampling Technique : Oversampling method using histogram bins and k-nearest neighbor interpolation. • Industrial Applicability Across Domains : Useful in various industries like healthcare and finance. • Improved Model Reliability : Makes predictive models more reliable and fair. • Superior Performance Compared to State-of-the-Art Methods : KNNOR-Reg consistently outperforms state of art oversampling techniques.