[4K3-IS-2f] Machine learning

[4K3-IS-2f-03] An Enhanced Two-Stage SFE with Adaptive Acceptance Selection and Flip-Flop Mutation for High-Dimensional Feature Selection

〇Yuefeng XU¹, Rui Zhong², Junqi Zhang³, Chao Zhang⁴, Jun YU¹ (1. Niigata University, 2. Hokkaido University, 3. Tongji University, 4. University of Toyama)

Keywords:Feature Selection, Evolutionary Computation, Large-scale Optimization

SFE, a prominent optimization algorithm for solving high-dimensional feature selection problems, exhibits strong exploitation capability using a single search agent. However, its ability for global exploration remains relatively weak. This paper introduces a flip-flop mutation mechanism and adaptive acceptance selection into the SFE algorithm and proposes a targeted two-stage improvement to enhance its performance in high-dimensional spaces. Specifically, the following enhancements are made to the original SFE algorithm: (1) the search process is divided into two distinct stages, each with a different focus, (2) a random flip-flop mutation mechanism is incorporated, and (3) mitigation of trapping in localized solutions by adaptive acceptance selection. We evaluate the proposed algorithm on 21 high-dimensional datasets and compare its performance with the original SFE algorithm and six state-of-the-art binary evolutionary algorithms. Experimental and statistical results demonstrate that these improvements significantly enhance the global exploration capability of SFE, making it more robust and effective for addressing high-dimensional feature selection challenges.