[1]韦艳艳 李陶深.基于特征选择的集成分类器抗噪性能分析[J].计算机技术与发展,2012,(11):161-164.
　WEI Yan-yan,LI Tao-shen.Anti-noise Performance Analysis of Classifiers Ensembles Based on Feature Selection[J].,2012,(11):161-164.
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基于特征选择的集成分类器抗噪性能分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年11期
页码:
161-164
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Anti-noise Performance Analysis of Classifiers Ensembles Based on Feature Selection
文章编号:
1673-629X（2012）11-0161-04
作者:
韦艳艳1 李陶深2
[1]广西民族大学信息科学与工程学院广西混杂计算与集成电路设计分析重点实验室[2]广西大学计算机与电子信息学院
Author(s):
WEI Yan-yan ;  LI Tao-shen
[1]Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, School of Information Science ＆ Engineering, Guangxi University for Nationalities[2]School of Computer ＆ Electronic Information, Guangxi University
关键词:
集成分类器; 特征选择; 随机子空间; 旋转森林; 抗噪性能
Keywords:
classifiers ensembles;  feature selection;  random subspace;  rotation forest;  anti-noise performance
分类号:
TP181
文献标志码:
A
摘要:
特征选择有助于增强集成分类器成员间的随机差异性，从而提高泛化精度。研究了随机子空间法（RandomSub-space）和旋转森林法（RotationForest）两种基于特征选择的集成分类器构造算法，分析讨论了两算法特征选择的方式与随机差异程度之间的关系。通过对UCI数据集引入噪声，比较两者在噪声环境下的分类精度。实验结果表明：当噪声增加及特征关联度下降时，基本学习算法及噪声程度对集成效果均有影响，当噪声增强到一定程度后。集成效果和单分类器的性能趋于一致
Abstract:
Feature selection encourages random differentiation of the members of the ensembles to improve generation accuracy. In this pa per, random subspace and rotation forest, two algorithms based on feature selection for constructing classifiers ensembles were researched and their relationship between ways of selecting features and its affection on diversity was discussed. By introducing noise into UCI data sets,compared anti-noise performance with different noisy level of two algorithms. Experimental results indicate that both base learning algorithms and noisy level affect the accuracy of an ensemble while noise increases and feature correlation decreases. In situation with higher classification noise, both ensembles and single classifier exhibit quite similar performance

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备注/Memo

备注/Memo:
广西自然科学基金项目（20lOGⅫsFAOl3127）;广西教育项目（201106LXl31）韦艳艳（1974-），女，广西贵港人，讲师，硕士，CCF会员，研究方向为机器学习、数据挖掘；李陶深，教授，博士，CCF高级会员，研究方向为网络路由及分布式计算
更新日期/Last Update: 1900-01-01