Equilibrium Optimizer for Feature Selection in Clustering

Feature selection (FS) is a crucial step in the data preprocessing phase of machine learning. FS is the process of selecting the relevent subset features from a large set of original features. In this study, we introduce an approach for feature selection in clustering problem relied on the Equilibrium Optimizer and the Gaussian Mixture Model. The main objective is to identify the subset that provides high-quality clustering, assessed using the Adjusted Rand Score (ARI) as the fitness function. The approach is tested on six available benchmarks with varying numbers of features and instances. The approach achieves superior results according to the ARI value and number of features. The comparison between the results with all features and the selected features highlights the effectiveness of the presented approach and the importance of feature selection. Further comparison with the Particle Swarm Optimization for feature selection confirms its superiority.