ASML-REG: Automated Machine Learning for Data Stream Regression
Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri · 2025
Online learning scenarios present a significant challenge for AutoML techniques due to the dynamic nature of data distributions, where the optimal model and configuration may change over time. While most research in machine learning for data streams has primarily focused on classification algorithms, regression methods have received significantly less attention. To address this gap, we propose ASML-REG, an Automated Streaming Machine Learning framework designed specifically for regression tasks on data streams. ASML-REG continuously explores a vast and diverse space of pipeline configurations, adapting to evolving data by focusing on the current best design, performing adaptive random searches in promising areas, and maintaining an ensemble of top-performing pipelines. Our experiments with real and synthetic datasets demonstrate that ASML-REG significantly outperforms current state-of-the-art data stream regression algorithms.