Incremental Learning Algorithms for Evolving Data Streams

Abstract

Incremental learning algorithms for evolving data streams have garnered significant attention due to the growing prevalence of real-time applications requiring adaptive models that can update continuously without retraining from scratch. Unlike batch learning, which assumes static datasets, incremental learning must cope with concept drift, unbounded data arrival, and limited computational resources. In this manuscript, we delve into the theoretical foundations of incremental updates, examine a broad spectrum of state-of-the-art algorithms—from Hoeffding Trees and Online Bagging to Adaptive Random Forests and Online Gradient Descent—and explore a variety of drift‐detection and adaptation strategies. We present a rigorous experimental framework featuring synthetic and real‐world data streams with controlled drift scenarios. Statistical comparisons reveal significant differences in accuracy, memory usage, update latency, and drift detection speed across algorithms, highlighting trade-offs between stability, reactivity, and resource consumption. Simulation studies under sudden, gradual, and incremental drift conditions demonstrate how ensemble methods with explicit drift handling maintain high predictive performance and robust adaptation, whereas simpler learners offer advantages under stringent resource constraints.

We conclude by outlining future research directions, including deep incremental models, automated hyperparameter tuning, and energy-efficient update mechanisms for edge deployments—paving the way for next-generation, adaptive learning systems in dynamic environments.