A comprehensive survey on recent metaheuristics for feature selection

Dokeroglu T., Deniz A., Kiziloz H. E.

NEUROCOMPUTING, vol.494, pp.269-296, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 494
  • Publication Date: 2022
  • Doi Number: 10.1016/j.neucom.2022.04.083
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, zbMATH
  • Page Numbers: pp.269-296
  • Keywords: Feature selection, Survey, Metaheuristic algorithms, Machine learning, Classification, ARTIFICIAL BEE COLONY, GRASSHOPPER OPTIMIZATION ALGORITHM, BIOGEOGRAPHY-BASED OPTIMIZATION, PARTICLE SWARM OPTIMIZATION, LEARNING-BASED OPTIMIZATION, MULTIOBJECTIVE FEATURE-SELECTION, GREY WOLF OPTIMIZATION, CROW SEARCH ALGORITHM, DIFFERENTIAL EVOLUTION, HARMONY SEARCH
  • Middle East Technical University Affiliated: Yes

Abstract

Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.