Adaptive Ensemble Learning for Real-Time Predictive Analytics in Streaming Big Data Environments

Abstract

This paper presents a novel adaptive ensemble learning framework designed for real-time predictive analytics in streaming big data environments. The proposed approach dynamically adjusts ensemble weights based on concept drift detection and maintains computational efficiency through selective model updates. Our methodology combines multiple base learners including Hoeffding Trees, Adaptive Random Forest, and Online Gradient Boosting to handle evolving data streams. Experimental results on synthetic and real-world datasets demonstrate superior performance compared to traditional static ensemble methods, achieving 15.3% improvement in prediction accuracy and 42% reduction in computational overhead. The framework successfully adapts to concept drift within an average response time of 2.1 seconds, making it suitable for mission-critical applications requiring real-time decision making.