A review of feature selection methods based on meta-heuristic algorithms

Abstract

ABSTRACT Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2022 that were designed and implemented on a wide range of different data for solving feature selection problem. Evaluation criteria, fitness functions and classifiers used and the time complexity of each method are also depicted. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. The results of this study and the identified research gaps can be used by researchers in this field.