A Dynamic Surrogate-Assisted Hybrid Breeding Algorithm for High-Dimensional Imbalanced Feature Selection

With the growing complexity of high-dimensional imbalanced datasets in critical fields such as medical diagnosis and bioinformatics, feature selection has become essential to reduce computational costs, alleviate model bias, and improve classification performance. DS-IHBO, a dynamic surrogate-assisted feature selection algorithm integrating relevance-based redundant feature filtering and an improved hybrid breeding algorithm, is presented in this paper. Departing from traditional surrogate-assisted approaches that use static approximations, DS-IHBO employs a dynamic surrogate switching mechanism capable of adapting to diverse data distributions and imbalance ratios through multiple surrogate units built via clustering. It enhances the hybrid breeding algorithm with asymmetric stratified population initialization, adaptive differential operators, and t-distribution mutation strategies to strengthen its global exploration and convergence accuracy. Tests on 12 real-world imbalanced datasets (4–98% imbalance) show that DS-IHBO achieves a 3.48% improvement in accuracy, a 4.80% improvement in F1 score, and an 83.85% reduction in computational time compared with leading methods. These results demonstrate its effectiveness for high-dimensional imbalanced feature selection and strong potential for real-world applications.