Feature Selection with Data Field

被引：0

作者：

Yuan Hanning ^{[1
]}

Wang Shuliang ^{[1
,2
]}

Li Ying ^{[2
]}

Fan Jinghua ^{[3
]}

机构：

[1] Beijing Inst Technol, Sch Software, Beijing 100081, Peoples R China

[2] State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China

[3] Wuhan Univ, Int Sch Software, Wuhan 430072, Peoples R China

来源：

CHINESE JOURNAL OF ELECTRONICS | 2014年 / 23卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Potential entropy; Data field; Feature selection; Dimension reduction; High-dimensional objects;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.

引用

页码：661 / 665

页数：5

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