A Comprehensive Study of Feature Selection Techniques in Machine Learning Models

Abstract

This paper explores the importance and applications of feature selection in machine learning models, with a focus on three main feature selection methods: filter methods, wrapper methods, and embedded methods. By comparing their advantages and limitations, the paper highlights how feature selection can improve model performance, reduce redundant features, minimize overfitting, and enhance computational efficiency. Additionally, the paper discusses the applications of feature selection across various domains, including healthcare, finance, and image processing, and examines how metrics such as accuracy, precision, and recall can assess the effectiveness of feature selection. As the complexity of datasets increases, the integration of feature selection with deep learning and explainable AI emerges as a key future direction, particularly in addressing scalability and fairness issues in large-scale and real-time applications. Finally, the paper concludes with an outlook on the future development and potential of feature selection in machine learning.