Feature Drift Detection in Live Data Stream Learning

Abstract

Feature drift occurs when the statistical properties or predictive relevance of input features change over
time in live data stream environments, causing machine learning models to gradually lose stability and
accuracy. Unlike concept drift, which alters the underlying target relationship, feature drift influences
how strongly individual features contribute to predictions, making detection and adaptation more subtle
and time-sensitive. This study presents a real-time drift monitoring framework that tracks dynamic
variation in feature distributions and feature attribution scores to identify early relevance shifts before
performance degradation becomes visible. An adaptive response mechanism then applies proportional
corrective strategies, ranging from incremental model updates to selective retraining or temporary
ensemble stabilization depending on drift intensity. Experimental evaluation shows that integrating drift
detection with context-aware adaptation preserves predictive performance more efficiently than uniform
retraining approaches. The results highlight that feature drift is best managed as a continuous systems
level process aligned with both data behavior and application workflow dynamics.