Graph-Based Feature Selection Methods for High-Dimensional Data Sets: State-of-the-Art Review

Feature selection is a fundamental process in machine learning and data analysis that significantly affects the performance of machine learning models, especially in high-dimensional datasets that often contain irrelevant or redundant features. These additional features can lead to reduced model accuracy and long computational time. An innovative and effective approach for feature selection in high-dimensional data is used in graph-based methods. This study comprehensively surveys different graph-based feature selection techniques for high-dimensional data to reduce complexity and improve accuracy. We have explored a wide range of graph-based feature selection methods and organized the existing works into traditional and modern methods. Also, we have compared existing methods in terms of prediction accuracy, computational complexity, scalability, flexibility, and speed. We also examined graph-based feature selection method applications and discussed the challenges and limitations associated with high-dimensional datasets. Finally, we specify unresolved challenges and potential future research paths to facilitate progress in this fastchanging domain.