Online Ensemble

Online ensemble learning focuses on building and adapting collections of machine learning models to handle dynamically changing data streams, a crucial capability for real-world applications where data distributions shift over time. Current research emphasizes developing efficient algorithms, such as those based on adaptive decision trees and multi-layer frameworks, that can achieve optimal performance while minimizing computational costs and memory usage, even in resource-constrained environments. These advancements address challenges like online label shift and concept drift, improving the robustness and adaptability of machine learning systems in diverse domains, including intrusion detection and online optimization. The resulting models offer improved accuracy and efficiency compared to traditional methods in non-stationary settings.