Adaptive Ensemble Multi-Objective Differential Evolution for High-Dimensional Gene Selection
TL;DR
Comprehensive experiments on five cancer microarray datasets demonstrate that AEMO-DEFS outperforms MOPSO, NSGA-III, and MOEA/D across all evaluation metrics, achieving superior Inverted Generational Distance, ϵ-indicator, classification accuracy, and significant feature reduction.
Abstract
This paper proposes Adaptive Ensemble Multi-Objective Differential Evolution for Feature Selection (AEMO-DEFS), a wrapper-based framework that simultaneously minimizes feature count and maximizes classification accuracy for high-dimensional gene expression data. AEMO-DEFS introduces two key adaptive mechanisms: (1) an ensemble of DE mutation strategies (rand/1/bin, best/1/bin, current-to-pbest/1/bin) with dynamic selection based on historical success, and (2) memory-based self-adaptation of the scaling factor F (using Cauchy distribution) and crossover rate CR (using Normal distribution). Non-dominated sorting combined with crowding distance maintains diverse Pareto-optimal solutions. Comprehensive experiments on five cancer microarray datasets demonstrate that AEMO-DEFS outperforms MOPSO, NSGA-III, and MOEA/D across all evaluation metrics, achieving superior Inverted Generational Distance (IGD), ϵ-indicator, classification accuracy, and significant feature reduction.