Equilibrium Optimizer and Henry Gas Solubility Optimization Algorithms for Feature Selection: Comparison Study

One of the most critical processes is feature selection, which eliminates features that may decrease classification performance and increase computational time. In this paper, we introduce and provide a comparison study between two algorithms, which are Equilibrium Optimizer (EO) and Henry Gas Solubility Optimization (HGSO) for Feature Selection (FS). The function objective of both algorithms are based on two main objectives, such as Error Rate (ER) and feature Reduction Rates (RR). In this comparative study, three classifiers (Naive Bayes NB, k-Nearest Neighbor KNN, and Random Forest RF) have been employed. The evaluation of the work was conducted on ten datasets, including Iris, Lung Cancer, Spambase, and Musk. The two algorithms show higher performances according to the accuracy and number of features, especially HGSOFS, which in turn shows its effectiveness and provides good results in the two tasks of FS when we compare it to the PSOFS (Particle Swarm Optimization for Feature Selection) and FAFS (Fire Fly for Feature Selection).