Drift Detection

Drift detection focuses on identifying changes in the statistical properties of data streams over time, a phenomenon known as concept drift, impacting the performance of machine learning models. Current research emphasizes developing unsupervised methods, particularly those leveraging deep learning representations (like autoencoders) and generative adversarial networks (GANs), to detect both global and localized drifts, as well as addressing the challenges posed by high-dimensional data and limited labeled data. Effective drift detection is crucial for maintaining the accuracy and reliability of machine learning systems across diverse applications, from wildlife monitoring and manufacturing to fraud detection and personalized recommendations, enabling timely model adaptation and improved system performance.