Li Genliang, Cui Yaxin, Su Jingyu
New Engineering Industry College, Putian University, Putian, Fujian, China.
Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian 351100, China.
PLoS One. 2025 May 16;20(5):e0318903. doi: 10.1371/journal.pone.0318903. eCollection 2025.
Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. The Grey Wolf Optimizer (GWO) is a well-known meta-heuristic algorithm rooted in swarm intelligence. It is widely used in various optimization problems due to its fast convergence and minimal parameter requirements. However, in the context of solving high-dimensional classification problems, GWO's global search capability is limited, and it is susceptible to getting trapped in local optima. To address this, we introduce an Adaptive Mechanism-based Grey Wolf Optimizer (AMGWO) for FS in high-dimensional classification. This approach encompasses a novel nonlinear parameter control strategy to balance exploration and exploitation effectively, thereby preventing the algorithm from converging prematurely. Additionally, an adaptive fitness distance balancing mechanism is proposed to prevent premature convergence and enhance search efficiency by selecting high-potential solutions. Lastly, an adaptive neighborhood mutation mechanism is designed to adjust mutation intensity adaptively during the search process, allowing AMGWO to more effectively find the global optimum. To validate the proposed AMGWO method, we assess its performance on 15 high-dimensional datasets and compare it with the original GWO and five of its variants in terms of classification accuracy, feature subset size, and execution speed, thus confirming the superiority of AMGWO.
特征选择（FS）是机器学习和数据挖掘的关键组成部分。其目标是从数据集中消除冗余和无关特征，从而提高分类器的性能。灰狼优化器（GWO）是一种基于群体智能的著名元启发式算法。由于其收敛速度快且参数要求最少，它被广泛应用于各种优化问题。然而，在解决高维分类问题时，GWO的全局搜索能力有限，容易陷入局部最优。为了解决这个问题，我们引入了一种基于自适应机制的灰狼优化器（AMGWO）用于高维分类中的特征选择。这种方法包含一种新颖的非线性参数控制策略，以有效平衡探索和利用，从而防止算法过早收敛。此外，还提出了一种自适应适应度距离平衡机制，通过选择高潜力解来防止过早收敛并提高搜索效率。最后，设计了一种自适应邻域变异机制，在搜索过程中自适应调整变异强度，使AMGWO能够更有效地找到全局最优解。为了验证所提出的AMGWO方法，我们在15个高维数据集上评估其性能，并在分类准确率、特征子集大小和执行速度方面将其与原始GWO及其五个变体进行比较，从而证实了AMGWO的优越性。
