A bibliometric analysis of feature selection techniques: trends, innovations, and future directions

Abstract

Feature selection techniques have become increasingly important in addressing the challenges of high dimensionality in machine learning and other artificial intelligence domains. In this study, we present a comprehensive bibliometric analysis of research on feature selection techniques over the past decade, focusing on mapping the intellectual structure, identifying emerging trends, and highlighting productive collaborations in the field. Using merged data from Scopus and Web of Science databases, we collected and analyzed 2,079 relevant documents published between 2014 and 2024, applying citation analysis, co-authorship networks, and keyword co-occurrence mapping. Our findings reveal that feature selection methodologies, including supervised, unsupervised, and hybrid approaches across filter, wrapper, and embedded techniques, have been widely applied across various domains. The authors who have most contributed to the development of these methods are primarily affiliated with institutions in China, India, and the USA. The insights provided by this analysis offer researchers and practitioners a valuable foundation for guiding future research directions in feature selection.