Concept Drift Adaptation

Concept drift adaptation addresses the challenge of maintaining machine learning model accuracy in the face of evolving data distributions. Current research focuses on developing robust methods for detecting and adapting to these shifts, employing techniques like ensemble methods, self-training with pseudo-labels, and dynamic model parameter adjustments using hypernetworks or autoencoders. These advancements are crucial for improving the reliability and longevity of machine learning systems across diverse applications, from malware detection and anomaly detection to industrial internet of things (IIoT) analytics and fairness-aware federated learning. The ultimate goal is to create more adaptable and resilient models that can effectively handle real-world data streams characterized by non-stationary distributions.