Hybrid Henry gas solubility optimization and the equilibrium optimizer for feature selection: real cases with Twitter spam detection

The rapid spread and daily usage of social networks have made them vulnerable to spammers. Therefore, detecting and eliminating spam and spammers has become more than necessary to reduce the risks that it poses to users’ security. In order to achieve this goal, it is crucial to determine the exact features that help identify and classify whether a user is spam or not. This paper proposes a wrapper-based method for selecting the most important features. It is based on combining two recent metaheuristic algorithms, the Henry Gas Solubility Optimization Algorithm (HGSO) and the Equilibrium Optimizer Algorithm (EO), with the goal of choosing a small and most influential subset of features that give good performance and help in the spammer profile detection process. For the purpose of showing the ability of the proposed method to achieve the desired goals, several comparisons are conducted on a modified Social Honeypot dataset. The first comparison is made between HGSOEO and the two algorithms (HGSO and EO) that were used to develop the proposed algorithm to prove the power of hybridization. The two next comparisons are made against some classical filter- and wrapper-based feature selection methods. The last comparison is carried out against some well-known metaheuristic algorithms for feature selection. Experiments and analysis of the results show that the proposed model is more accurate than the algorithms and methods that we compared it to.