A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

Automated summary

Feature selection is a popular technique in machine learning to improve classification accuracy by extracting optimal features. This study proposes a self-adaptive quantum equilibrium optimizer with artificial bee colony (SQEOABC) for feature selection. Experimental results on benchmark datasets and a real-world COVID-19 problem demonstrate the effectiveness and superiority of the SQEOABC algorithm compared to other metaheuristic algorithms and variants of equilibrium optimizer.