Feature Selection (FS) is regarded as an important preprocessing technique with the main aim of discarding irrelevant and redundant features on the premise of improving or retaining classification accuracy. As one of the most popular FS methods, the swarm intelligent (SI) algorithm based FS method faces great challenges in solution quality. Given this, a novel probe mechanism based particle swarm optimization (PPSO) is proposed. The probe mechanism is proposed to test the dataset with different filter methods by employing a part of features to generate high-quality initial population, thus playing a critical role in enhancing the final results of the algorithm. Next, the repeated binarized solution avoid mechanism prevents the same binarized solution from repeating compute the fitness function, which can improve the exploration capability. Additionally, a volcanic eruption mechanism based on the updated information of the global best solution is designed to improve the performance in convergence rate and search precision. In the experiment section, 14 UCI datasets are employed to assess the performance of the PPSO. The outcomes show that the designed strategies can significantly improve the original PSO, and the PPSO is more efficient than the other comparative algorithms for solving the FS problems. In addition, the impact of each strategy is analyzed, and the volcanic eruption mechanism is the most efficient among the three strategies.

特征选择（FS）被认为是一种重要的预处理技术，其主要目的是在提高或保持分类精度的前提下丢弃不相关和冗余的特征。作为最流行的FS方法之一，基于群体智能（SI）算法的FS方法在解质量方面面临着巨大的挑战。鉴于此，提出了一种基于粒子群优化（PPSO）的新型探针机制。探针机制的提出是通过利用部分特征来测试不同过滤方法的数据集，以生成高质量的初始种群，从而对增强算法的最终结果起到关键作用。其次，重复二值化解避免机制防止相同的二值化解重复计算适应度函数，这可以提高探索能力。此外，还设计了基于全局最佳解更新信息的火山喷发机制，以提高收敛速度和搜索精度。在实验部分，使用 14 个 UCI 数据集来评估 PPSO 的性能。结果表明，所设计的策略可以显着改善原始PSO，并且PPSO在解决FS问题方面比其他对比算法更有效。此外，分析了每种策略的影响，火山喷发机制是三种策略中最有效的。