Feature Selection Based on RS-PSO-SVM

CUI Wen-yan; MENG Xiang-ru; LI Ji-zhen; WANG Ming-ming; CHEN Tian-ping; WANG Kun

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Feature Selection Based on RS-PSO-SVM

更新时间：2026-09-04

- Feature Selection Based on RS-PSO-SVM
- Vol. 32, Issue 1, Pages: 120-123(2015)
- 作者机构：
  
  1. 1 空军工程大学机构信息与导航学院,陕西,西安,710077
  2. 2 94872部队司令部, 江西樟树,331204
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015
- 稿件说明：
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崔文岩, 孟相如, 李纪真, 王明鸣, 陈天平, 王坤. 基于粗糙集粒子群支持向量机的特征选择方法[J]. 微电子学与计算机, 2015, 32(1): 120-123.

CUI Wen-yan, MENG Xiang-ru, LI Ji-zhen, et al. Feature Selection Based on RS-PSO-SVM[J]. 2015, 32(1): 120-123.
崔文岩, 孟相如, 李纪真, 王明鸣, 陈天平, 王坤. 基于粗糙集粒子群支持向量机的特征选择方法[J]. 微电子学与计算机, 2015, 32(1): 120-123. DOI：

CUI Wen-yan, MENG Xiang-ru, LI Ji-zhen, et al. Feature Selection Based on RS-PSO-SVM[J]. 2015, 32(1): 120-123. DOI：

摘要

将Filter型粗糙集属性约简方法与PSO-SVM方法相结合

提出一种新的粗糙集粒子群支持向量机(RS-PSO-SVM)特征选择方法.给出了该方法的特征选择具体步骤

并对比分析了所提方法的性能.仿真实验表明:提出的RS-PSO-SVM特征选择方法是有效的

在保证所选特征集为最优情况下

极大地缩短所用时间

可以将其应用在多维数据的特征选择中.

Abstract

A new RS-PSO-SVM feature selection combined Filter Rough Set attribute reduction with PSO-SVM was proposed. The specific steps of the new method were given

and the performance was analyzed contrasted with other two methods. Simulation experiments show that

the proposed feature selection method is effective which can reduce time consumed largely and ensure the selected feature set is the best. The proposed method can be used in the feature selection of multidimensional data.

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