Design and Implementation of a Dynamic Adaptive Concept Drift Processing System

Abstract

To address the critical industry pain points in streaming data scenarios across industrial production, intelligent transportation and other related fields—namely continuous performance degradation of classification models induced by concept drift, the absence of dedicated closed-loop drift adaptation processing capability in mainstream stream processing systems, poor engineering implementability of specialized tools, and the inherent difficulty in balancing real-time performance and classification accuracy—this paper designs and implements a Dynamic Adaptive Concept Drift Processing System (DACPS) for streaming data classification. The system adopts a hierarchical modular architecture, integrating five core functional modules: streaming data preprocessing, real-time concept drift detection, dynamic sample optimization, incremental model training, and full-process management and control. This architecture enables fully automated and adaptive processing for the end-to-end workflow of streaming data classification and prediction. Extensive multi-dimensional functional tests, specialized performance tests, and scenario-based validation demonstrate that the system can stably adapt to diverse types of concept drift scenarios, and meets all design specifications in terms of classification accuracy, convergence speed, operating efficiency, and resource occupation control. Meanwhile, the system is equipped with an accessible visual interactive interface, making it widely adaptable to the streaming data classification and processing requirements across multiple application domains.