Optimization of a subset of features based on modified particle swarm optimization

HOU Da-jun; ZHU Wei-xing

doi:10.13873/j.1000-97872010.09.013

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Optimization of a subset of features based on modified particle swarm optimization

更新时间：2025-09-11

- Optimization of a subset of features based on modified particle swarm optimization
- Vol. 29, Issue 9, Pages: 64-66(2010)
- 作者机构：
  
  江苏大学电气信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872010.09.013
  CLC： TP391.41
- Published：2010
- 稿件说明：
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[1]侯大军,朱伟兴.基于改进粒子群算法的最优特征子集研究[J].传感器与微系统,2010,29(09):64-66.

HOU Da-jun, ZHU Wei-xing. Optimization of a subset of features based on modified particle swarm optimization[J]. 2010, 29(9): 64-66.
[1]侯大军,朱伟兴.基于改进粒子群算法的最优特征子集研究[J].传感器与微系统,2010,29(09):64-66. DOI： 10.13873/j.1000-97872010.09.013.

HOU Da-jun, ZHU Wei-xing. Optimization of a subset of features based on modified particle swarm optimization[J]. 2010, 29(9): 64-66. DOI： 10.13873/j.1000-97872010.09.013.

摘要

特征选择是模式识别系统的难点。针对高维数据对象

先运用改进粒子群优化（PSO）算法快速、有效地从特征样本中提取一组最优特征子集

然后采用最小二乘支持向量机（LSSVM）分类器对最优特征子集进行分类

验证特征选择的好坏。经大量实验验证

在保证分类正确率的前提下

该方法有效提高了特征选择效率。

Abstract

Feature selection is a very difficult problems for mode recognition system.For high dimension data

the modified particle swarm optimization（PSO） algorithm was effectively and quickly applied to the feature extraction of the optimum samples.The optimum samples were recognized by the support vector machine classifier

which is checked how to use the feature selection.Experimental results on the database show this method can guarantee the correct rate of classification and improve the efficiency of feature selection.

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references

内嵌多准则的遗传算法用于尿沉渣红白细胞的特征选择 [J]. 李勇明,周頔,曾孝平,韩庆文. 系统仿真学报 . 2008(14)

Genetic Feature Selection for Texture Classification [J]. PAN Li ZHENG Hong ZHANG Zuxun ZHANG Jianqing. Geo-Spatial Information Science . 2004(03)

Feature selection based-on genetic algorithm for image annotation [J] . Jianjiang Lu,Tianzhong Zhao,Yafei Zhang. Knowledge-Based Systems . 2008 (8)

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