Multi-objective metaheuristic optimization algorithms for wrapper-based feature selection: a literature survey

In the data mining and machine learning (ML) discipline, feature selection problem is considered among many researchers in recent times. Feature process targets to minimize set number maximize performance accuracy by identifying optimal features. Multiple objectives are while hence multi-objective metaheuristic optimization algorithms (MOMOAs) applied. this study, literature review performed MOMOAs-for solving wrapper (WFS). The for WFS discuss challenges faced problem. on all relevant studies published last 12 years [2009-2022]. A detailed overview of preliminaries, MOMOAs-WFS, role classifier presented. outcome highlight existing works related using MOMOAs. Finally, research areas improvement identified emphasized scientists survey field