A Comprehensive Survey of Feature Selection Techniques based on Whale Optimization Algorithm

Abstract A large number of features is the main problem in big data, leading to the curse of dimensionality. Meanwhile, feature selection is suggested as a solution. The process of feature selection consists of adding relevant features to a neural model and eliminating irrelevant or redundant ones. The feature selection community has recently been drawn to swarm intelligence techniques due to their simplicity and potential global search capabilities. A straightforward overview of the newest research in the feature selection field is provided here using a nature-inspired metaheuristic method called Whale Optimization Algorithm (WOA). Research is expected to be presented in terms of various types of state-of-the-art methods and their advantages and disadvantages, encouraging researchers to investigate more advanced approaches. A discussion of possible limitations and issues for future research is included as well as guidance for practitioners on selecting appropriate methods for real-world situations.