A Novel Single-Feature and Synergetic-Features Selection Method by Using ISE-Based KDE and Random Permutation  

作　　者：ZHANG Jingxiang WANG Shitong 

机构地区：[1]School of Digital Media, Jiangnan University [2]School of Science, Jiangnan University

出　　处：《Chinese Journal of Electronics》2016年第1期114-120,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61202311,No.61300151);the Research and Development Frontier Grant of Jiangsu Province(No.BY2013015-02);Doctor Candidate Foundation of Jiangnan University(No.JUDCF13031);the 2013 Postgraduate Students Creative Research Fund of Jiangsu Province(No.CXLX13 748)

摘　　要：The Integrated square error(ISE), as a robust criterion for measuring the difference of densities between two datasets, have been commonly used in pattern recognition. In this paper, two different criteria for evaluating candidate feature subsets are investigated: first, a novel supervised feature selection criterion based on ISE and random permutation of a single feature is proposed,which presents a feature ranking criterion to measure the importance of each feature by computing the ISE over the feature space. Second, a synergetic feature selection criterion is developed. Experimental results on synthetic and real data set show the superior or at least comparable performance compared with existing feature selection algorithms.The Integrated square error（ISE）, as a robust criterion for measuring the difference of densities between two datasets, have been commonly used in pattern recognition. In this paper, two different criteria for evaluating candidate feature subsets are investigated： first, a novel supervised feature selection criterion based on ISE and random permutation of a single feature is proposed,which presents a feature ranking criterion to measure the importance of each feature by computing the ISE over the feature space. Second, a synergetic feature selection criterion is developed. Experimental results on synthetic and real data set show the superior or at least comparable performance compared with existing feature selection algorithms.

关 键 词：Feature selection Integrated squared er-ror Random permutation. 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]