摘要
提出了一种新的PSO特征选取方法.以粒子对应特征组合的同类近邻样本和异类近邻样本间的距离关系作为类别可分性和粒子适应度函数.以适应度函数加权的群体历史最佳、粒子历史最佳和粒子邻域内最佳个体信息共同指导粒子运动方向,搜索类内紧密、类间分离的最佳特征组合;同时,利用加权集成方法对PSO特征选取方法进行集成,以提高特征选取方法的稳定性和鲁棒性.在5个高维数据集上的特征选取实验结果表明集成PSO特征选取方法的有效性和可行性.
A new PSO algorithm is proposed in this paper for feature selection. Distances within the same class and between different classes are used as the index for distinguishing different classes, and thus can be used to construct the fit- ness function of particles in PSO. The direction of particles for searching optimal features which can result in close intra-class distance and far inter-class distance is determined by the current best solution of the particle and the optimal individual in particle neighborhood, weighted by the fitness function. Meanwhile, the PSO algorithm is aggregated by the weighted voting method to improve its stability and robustness. The experiment results on 5 high dimensional datasets show that the ensemble PSO algorithm is effective and feasible.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2016年第4期995-1002,共8页
Acta Electronica Sinica
基金
国家自然科学基金(No.61475071
No.61173068
No.10172043)
教育部博士点基金(No.20093218110024)
江苏省自然科学基金青年基金(No.BK20141032)
国家质检总局科技项目(No.2013QK194)
安徽省自然科学基金(No.1608085QF157)
关键词
特征选取
PSO
集成方法
分类
feature selection
PSO （ particle swarm optimization）
ensemble method
classification
