Abstract

This paper comprehensively analyzes objective functions used in feature selection, a critical aspect of machine learning. We conducted a systematic literature review, categorizing objective functions into single-objective and multi-objective, with further classification into pure and weighted multi-objective functions. Our study spans research from 2019 to 2023, analyzing 161 articles. We found that weighted multi-objective functions are most prevalent, highlighting their efficacy in balancing model performance and complexity. This work offers a detailed classification of these functions, contributing to a deeper understanding of their role and effectiveness in feature selection challenges. Our findings illuminate trends and preferences in objective function usage, providing valuable insights for researchers and practitioners in machine learning.