Ensemble Feature Selection Method Based on Neighborhood Information and PSO Algorithm

LIU Quan-jin; ZHAO Zhi-min; LI Ying-xin; YU Xiao-lei

doi:10.3969/j.issn.0372-2112.2016.04.034

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Ensemble Feature Selection Method Based on Neighborhood Information and PSO Algorithm

更新时间：2025-07-16

- Ensemble Feature Selection Method Based on Neighborhood Information and PSO Algorithm
- Acta Electronica Sinica Vol. 44, Issue 4, Pages: 995-1002(2016)
- 作者机构：
  
  1. 南京航空航天大学理学院,江苏,南京,210016
  2. 安庆师范学院物理与电气工程学院,安徽,安庆,246011
  3. 北京经纬纺机新技术有限公司,北京,100176
  4. 北京市轻纺机械机器视觉工程技术研究中心,北京,100176
  5. 江苏省标准化研究院,江苏,南京,210029
  6. 南京航空航天大学理学院,江苏,南京,210016
  7. 安庆师范学院物理与电气工程学院,安徽,安庆,246011
  8. 北京经纬纺机新技术有限公司,北京,100176
  9. 北京市轻纺机械机器视觉工程技术研究中心,北京,100176
  10. 江苏省标准化研究院,江苏,南京,210029
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61475071, No.61173068, No.10172043);Ph.D. Programs Foundation of Ministry of Education of China (No.20093218110024);Natural Science Foundation of Jiangsu Province Youth Fund (No.BK20141032);Science and Technology Project of AQSIQ (No.2013QK194);Natural Science Foundation of Anhui Province (No.1608085QF157)
- DOI：10.3969/j.issn.0372-2112.2016.04.034
  CLC： TP391
- Published：2016
- 稿件说明：
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LIU Quan-jin, ZHAO Zhi-min, LI Ying-xin, et al. Ensemble Feature Selection Method Based on Neighborhood Information and PSO Algorithm[J]. Acta Electronica Sinica, 2016, 44(4): 995-1002.
DOI：

LIU Quan-jin, ZHAO Zhi-min, LI Ying-xin, et al. Ensemble Feature Selection Method Based on Neighborhood Information and PSO Algorithm[J]. Acta Electronica Sinica, 2016, 44(4): 995-1002. DOI： 10.3969/j.issn.0372-2112.2016.04.034.

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.

Abstract

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 fitness 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.

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