Shi Li

Binary sea-horse optimizer with staged adjustment and improved breeding strategy to solve high-dimensional cancer gene expression data feature selection

Parallel Dynamic Adaptive Transfer Function Based on Population Diversity to Solve High‐Dimensional Cancer Gene Expression Data Feature Selection Problem

In cancer genomics research, feature selection (FS) of high‐dimensional gene expression data is of great significance to improve classification accuracy and reduce feature number. Aiming at the limitations of traditional transfer functions, such as nonadaptability and being prone to fall into local optimum when deali...

MOPSO-ES: Multi-objective particle swarm optimization algorithm based on niche technology and dynamic elliptical segmentation with distance screening mechanism for high-dimensional feature selection

Feature selection (FS) is a core preprocessing technique for improving data quality, and it often needs to strike a balance between the conflicting goals of maximizing classification accuracy and minimizing feature dimension. A multi-objective particle swarm optimization algorithm (MOPSO-ES) based on niche technology a...