Multiscale-adaptive and Size-adaptive PSO-based Feature Selection for Gene Expression Analysis

Abstract

In gene expression analysis, high dimension low sample size data limits the applicability of deep learning, motivating increasing interest in variable-length evolutionary algorithm–based feature selection methods (VLEAs). However, existing VLEAs suffer from unreliable single-metric discrimination under dynamic search-space variation, mismatches between population size and search space dimensionality, and particle performance degradation after search space changes. To this end, a Multiscale-adaptive and Size-adaptive Particle Swarm Optimization (MASA-PSO) is proposed for gene expression analysis. MASA-PSO adopts a multiscale-adaptive weighting framework to explore feature subsets that distinguish between-class sample distributions during search spaces changes, and theoretically proves it enables the collaborative evaluation of multiple metrics. Meanwhile, it proposes an adaptive population division that explicitly models the functional relationship between the population size and the search space to resolve the mismatch. Furthermore, a particle degradation phenomenon impairing the performance of VLEAs is observed and alleviated through a hybrid elite strategy. Experiments on ten gene expression datasets verify that MASA-PSO outperforms state-of-the-art methods in classification accuracy while capturing smaller feature subsets.