Enhancing AutoML with Algorithm Selection: A Path to Better Performance
Machine learning algorithms have been widely adopted across a variety of application domains. However, training models and evaluating their performance is often time-consuming and computationally expensive. Furthermore, the identification of (near-)optimal learning strategies, including design choices and hyperparameter configurations, remains a significant challenge, as these decisions substantially influence the quality of learning outcomes. To address these issues, Automated Machine Learning (AutoML) has been developed with the aim of minimizing manual intervention and automating the creation of ready-to-use machine learning pipelines. Despite the widespread adoption of numerous successful AutoML systems, their performance is observed to vary across different datasets and learning scenarios. Algorithm selection has been proposed as a solution to this limitation, enabling the recommendation of algorithms on a per-instance basis. In this study, algorithm selection is utilized to enhance the capabilities of existing AutoML frameworks with minimal additional effort. A comprehensive empirical evaluation is conducted on 39 diverse tasks from the OpenML platform, involving 6 state-of-the-art AutoML methods. The results demonstrate that the integration of algorithm selection not only amplifies the strengths of current AutoML systems but also leads to improved performance and robustness. These findings highlight the practicality and efficacy of algorithm selection as a critical advancement for the next generation of AutoML technologies.
