基于最近邻互信息的特征选择算法

被引:17
作者
王晨曦 [1 ]
林耀进 [2 ]
刘景华 [2 ]
林梦雷 [2 ]
机构
[1] 漳州职业技术学院计算机工程系
[2] 闽南师范大学计算机学院
关键词
特征选择; 最近邻; 互信息; 邻域互信息;
D O I
暂无
中图分类号
TP301.6 [算法理论];
学科分类号
080201 [机械制造及其自动化];
摘要
针对邻域信息系统的特征选择模型存在人为设定邻域参数值的问题。分别计算样本与最近同类样本和最近异类样本的距离,用于定义样本的最近邻以确定信息粒子的大小。将最近邻的概念扩展到信息理论,提出最近邻互信息。在此基础上,采用前向贪心搜索策略构造了基于最近邻互信息的特征算法。在两个不同基分类器和八个UCI数据集上进行实验。实验结果表明:相比当前多种流行算法,该模型能够以较少的特征获得较高的分类性能。
引用
收藏
页码:74 / 78
页数:5
相关论文
共 10 条
[1]
基于邻域粒化和粗糙逼近的数值属性约简 [J].
胡清华 ;
于达仁 ;
谢宗霞 .
软件学报, 2008, (03) :640-649
[2]
Feature selection via neighborhood multi-granulation fusion[J] Yaojin Lin;Jinjin Li;Peirong Lin;Guoping Lin;Jinkun Chen Knowledge-Based Systems 2014,
[3]
Quality of information-based source assessment and selection[J] Yaojin Lin;Xuegang Hu;Xindong Wu Neurocomputing 2014,
[4]
MIFS-ND: A mutual information-based feature selection method[J] N. Hoque;D.K. Bhattacharyya;J.K. Kalita Expert Systems With Applications 2014,
[5]
Time-efficient estimation of conditional mutual information for variable selection in classification[J] Diman Todorov;Rossi Setchi Computational Statistics and Data Analysis 2014,
[6]
Neighborhood effective information ratio for hybrid feature subset evaluation and selection[J] Wenzhi Zhu;Gangquan Si;Yanbin Zhang;Jingcheng Wang Neurocomputing 2013,
[7]
An efficient rough feature selection algorithm with a multi-granulation view[J] Jiye Liang;Feng Wang;Chuangyin Dang;Yuhua Qian International Journal of Approximate Reasoning 2012,
[8]
Measuring relevance between discrete and continuous features based on neighborhood mutual information[J] Qinghua Hu;Lei Zhang;David Zhang;Wei Pan;Shuang An;Witold Pedrycz Expert Systems With Applications 2011,
[9]
Neighborhood rough set based heterogeneous feature subset selection[J] Qinghua Hu;Daren Yu;Jinfu Liu;Congxin Wu Information Sciences 2008,
[10]
Consistency-based search in feature selection[J] Manoranjan Dash;Huan Liu Artificial Intelligence 2003,