Feature Selection with Data Field

作     者：YUAN Hanning WANG Shuliang LI Ying FAN Jinghua 

作者机构：School of SoftwareBeijing Institute of Technology State Key Laboratory of Information Engineering in SurveyingMapping and Remote Sensing International School of Software Wuhan University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2014年第23卷第4期

页      面：661-665页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：supported by the the National Natural Science Foundation of China(No.61173061,No.71201120) the Doctoral Fund of Higher Education(No.20121101110036) 

主　　题：Potential entropy Data field Feature selection Dimension reduction High-dimensional objects 

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.