Adaptive Immune Optimization Algorithm: Combining Mutual Information and Differential Evolution-based Mutation for Local Feature Selection on Microarray Data.

Microarray data, characterized by high dimensionality and small sample sizes, poses significant challenges in identifying genes relevant for disease classification and prognosis. Our study proposes a one-stage filter local feature selection based on an immune algorithm to efficiently select relevant features in microarray data. It embeds two filter-based feature selection methods into an improved discrete immune algorithm and allocates feature subsets for different regions with considering local sample behaviors. The experimental results demonstrate the superiority of our method compared with well-known global and local feature selection methods on five microarray datasets.