Understanding the Rise of Automated Machine Learning: A Global Overview and Topic Analysis

Abstract

Automated Machine Learning (AutoML) has become an important area of modern artificial intelligence, enabling computers to automate the selection, training, and tuning of machine learning models and offering exciting opportunities for enhanced decision-making across various sectors. As the global adoption of machine learning technologies grows, it has been observed that also the importance of understanding the development and proliferation of AutoML research continues to grow, as highlighted by the increased number of scientific papers published each year. The present paper explores the scientific literature associated with AutoML with the aim of highlighting emerging trends, key topics, and collaborative networks that have contributed to the rise of this field. Using data from the Institute for Scientific Information (ISI) Web of Science database, we analyzed 920 papers dedicated to AutoML research, extracted based on specific keywords. A key finding is the significant annual growth rate of 87.76%, which underscores the increasing interest of the academic community in AutoML. Furthermore, we employed n-gram analysis and reviewed the most cited papers in the database, providing a comprehensive bibliometric overview of the current state of AutoML research. Additionally, topic discovery has been conducted through the use of Latent Dirichlet Allocation (LDA) and BERTopic, showcasing the interest of the researchers in this area. The analysis is completed by a review of the most cited papers, as well as discussions of the papers in the research areas associated with this AutoML. These findings offer valuable insights into the evolution of AutoML and highlight the key challenges and opportunities addressed by the academic community in this rapidly growing field.