Twin Q-learning-driven forest ecosystem optimization for feature selection

Abstract

Feature selection (FS) enhances the performance of the classification model by selecting relevant features and discarding unnecessary ones. Due to the efficiency of metaheuristic algorithms in solving FS problems, they have drawn much attention. However, the previous metaheuristic-based FS methods have drawbacks, such as easily falling into local optima and limited utilization of FS characteristics. To address these problems, we propose a novel twin Q-learning-driven forest ecosystem optimization named TQFEO for FS problems. Initially, an ordinal number initialization strategy is developed to guarantee the quality of initial individuals at the initial stage. Specifically, a twin Q-learning-driven forest ecosystem is constructed to ensure the algorithm's adaptive capability. Furthermore, a fitness-variance-evaluation-based status detection strategy is proposed to perceive optimization status. If an abnormality is detected, low-quality individuals are to be processed. Finally, a Manhattan distance guides position update and elite random walk strategy is designed to maintain population diversity and accelerate the convergence rate. Experimental results on 20 benchmark datasets across various domains demonstrate that TQFEO outperforms conventional and recent metaheuristic algorithms.