GMMCL: Adaptive Concept Drift in Data Streams with Gaussian Mixture Models based on Contrastive Learning

Classical classification methods often fail in dynamic environments where data distributions shift over time, known as concept drift. Applications like flight delay prediction and weather forecasting require handling such dynamic data streams. Concept drift can be either virtual, affecting unconditional probability distributions, or real, affecting conditional distributions. While most research focuses on real drift, virtual drift and noise also degrade classifier performance. In this paper, we propose gaussian mixture models based on contrastive learning (GMMCL), a novel approach that integrates noise handling, contrastive learning, drift detection, and gaussian mixture models. Our method significantly enhances adaptability to noisy and drifting data streams, outperforming mainstream approaches across twelve synthetic and real-world datasets. This provides a robust solution for managing concept drift and noise in dynamic classification tasks.