Enhanced NSGA-II-based feature selection method for high-dimensional classification

Feature selection in high-dimensional data faces significant challenges owing to large and discrete decision spaces. In this study, we propose a feature selection method based on the nondominated sorting genetic algorithm-II (NSGA-II) to enhance the performance of feature selection in high-dimensional data. This study makes four contributions: 1) The sparse initialization strategy is used to sparsen the search space and accelerate the convergence speed of the algorithm; 2) the guided selection operator is employed to strike a balance between exploration and exploitation abilities; 3) an intra-population evolution-based mutation operator dynamically shrinks the search space; and 4) a greedy repair strategy is adopted to generate improved feature subsets. The proposed method was validated on 15 publicly available high-dimensional datasets and compared with eight competitive multi-objective feature selection methods. The results demonstrate that the proposed method can achieve superior classification accuracy in a shorter time, with a smaller subset of features containing less redundancy.