Feature Selection Methods for Data Dimensionality Reduction - Dehghani, Sara; Malekhosseini, Razieh; Bagherifard, Karamollah; Yaghoubyan, Seyed Hadi (Management Strategies and Engineering Sciences, 2026)
Despite creating opportunities, platforms with large-scale data also pose significant computational challenges. An issue with high-dimensional data is that in many cases, not all of the data's features are important or vital for uncovering the knowledge hidden within it. Due to this, reducing the dimensionality of data remains a significant topic in many areas of data mining. Using feature selection techniques is one effective method for reducing the dimensionality of data. During the process of feature selection, a subset of the original features is selected by eliminating irrelevant and redundant features. This article analyzes and categorizes different feature selection techniques from different perspectives. After that, it provides an overview of data clustering concepts and categorizes different clustering algorithms. This article also investigates the use of optimization algorithms in feature selection methods and presents methods based on this approach. Next, this article compares and analyzes feature selection methods, emphasizing their strengths and weaknesses.