A novel feature selection method based on a multi-swarm particle swarm optimization (MSPSO) is proposed in this paper. The canonical particle swarm optimization (PSO) has been widely used for feature selection problems. However, PSO suffers from stagnation in local optimal solutions and premature convergence in complex feature selection problems. This paper employs the multi-swarm topology in which the population is split into several small-sized sub-swarms. Particles in each sub-swarm update their positions with the guidance of the local best particle in its own sub-swarm. In order to promote information exchange among the sub-swarms, an elite learning strategy is introduced in which the elite particles in each sub-swarm learn from the useful information found by other sub-swarms. Moreover, a local search operator is proposed to improve the exploitation ability of each sub-swarm. MSPSO is able to improve the population diversity and better explore the entire feature space. The performance of the proposed method is compared with six PSO based wrappers, three traditional wrappers, and three popular filters on eleven datasets. Experimental results verify that MSPSO can find feature subsets with high classification accuracies and smaller numbers of features. The analysis of the search behavior of MSPSO demonstrates its effectiveness on maintaining population diversity and finding better feature subsets. The statistical test demonstrates that the superiority of MSPSO over other methods is significant.

中文翻译：

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一种用于特征选择的新型多群粒子群优化

本文提出了一种基于多群粒子群优化（MSPSO）的特征选择方法。经典粒子群优化（PSO）已被广泛用于特征选择问题。然而，PSO 存在局部最优解停滞和复杂特征选择问题过早收敛的问题。本文采用多群拓扑，其中人口被分成几个小规模的子群。每个子群中的粒子在其子群中局部最佳粒子的指导下更新它们的位置。为了促进子群之间的信息交换，引入了精英学习策略，其中每个子群中的精英粒子从其他子群发现的有用信息中学习。而且，提出了一种局部搜索算子来提高每个子群的开发能力。MSPSO 能够提高种群多样性并更好地探索整个特征空间。在 11 个数据集上将所提出方法的性能与六个基于 PSO 的包装器、三个传统包装器和三个流行的过滤器进行了比较。实验结果验证了 MSPSO 可以找到具有较高分类精度和较少特征数量的特征子集。对 MSPSO 搜索行为的分析证明了其在保持种群多样性和寻找更好的特征子集方面的有效性。统计检验表明，MSPSO 相对于其他方法的优越性是显着的。在 11 个数据集上将所提出方法的性能与六个基于 PSO 的包装器、三个传统包装器和三个流行的过滤器进行了比较。实验结果验证了 MSPSO 可以找到具有较高分类精度和较少特征数量的特征子集。对 MSPSO 搜索行为的分析证明了其在保持种群多样性和寻找更好的特征子集方面的有效性。统计检验表明，MSPSO 相对于其他方法的优越性是显着的。在 11 个数据集上将所提出方法的性能与六个基于 PSO 的包装器、三个传统包装器和三个流行的过滤器进行了比较。实验结果验证了 MSPSO 可以找到具有较高分类精度和较少特征数量的特征子集。对 MSPSO 搜索行为的分析证明了其在保持种群多样性和寻找更好的特征子集方面的有效性。统计检验表明，MSPSO 相对于其他方法的优越性是显着的。对 MSPSO 搜索行为的分析证明了其在保持种群多样性和寻找更好的特征子集方面的有效性。统计检验表明，MSPSO 相对于其他方法的优越性是显着的。对 MSPSO 搜索行为的分析证明了其在保持种群多样性和寻找更好的特征子集方面的有效性。统计检验表明，MSPSO 相对于其他方法的优越性是显着的。