DACD: a dynamic adaptive concept drift learning method for streaming data

Abstract

Concept drift in streaming data presents a persistent challenge for constructing robust analytical models. Traditional algorithms often exhibit limitations in complex scenarios, performing suboptimally with diverse classification tasks and binary-featured data. This paper introduces the Dynamic Adaptive Concept Drift learning method (DACD) to address these issues. DACD employs a window-based Cumulative Sum (CUSUM) algorithm for drift detection, coupled with a neural network that is updated based on the detected drift type. An adaptive sample processing strategy further enhances model resilience by filtering and augmenting the training data under temporal constraints. The framework selects suitable loss functions optimized with class weights and integrates batch processing and learning rate schedulers to improve training stability. On various benchmark datasets, DACD demonstrates strong performance in accuracy, convergence, and robustness, providing a practical solution for real-world streaming data analysis.