Supervised Feature Selection With a Stratified Feature Weighting Method

Chen, Renjie1; Sun, Ning2; Chen, Xiaojun3; Yang, Min4; Wu, Qingyao1

2018

10.1109/ACCESS.2018.2815606

IEEE Access   影响因子和分区

2169-3536

Feature selection has been a powerful tool to handle high-dimensional data. Most of these methods are biased toward the highest rank features which may be highly correlated with each other. In this paper, we address this problem proposing stratified feature ranking (SFR) method for supervised feature ranking of high-dimensional data. Given a dataset with class labels, we first propose a subspace feature clustering (SFC) to simultaneously identify feature clusters and the importance of each feature for each class. In the SFR method, the features in different feature clusters are separately ranked according to the subspace weight produced by SFC. After that, we propose a stratified feature weighting method for ranking the features such that the high rank features are both informative and diverse. We have conducted a series of experiments to verify the effectiveness and scalability of SFC for feature clustering. The proposed SFR method was compared with six feature selection methods on a set of high-dimensional datasets and the results show that SFR was superior to most of these feature selection methods.

Data mining computational and artificial intelligence clustering algorithms feature selection

SCI ; EI

英语

其他

CCF-Tencent Open Research Fund[RAGR20170105]

Computer Science ; Engineering ; Telecommunications

Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000428961000001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20181204918854

Clustering algorithms ; Computer programming ; Correlation methods ; Data mining ; Electronic mail ; Job analysis ; Linear programming

Computer Programming:723.1 ; Data Processing and Image Processing:723.2 ; Information Sources and Analysis:903.1 ; Mathematical Statistics:922.2

Web of Science

1.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China
2.South Univ Sci & Technol, Lab & Equipment Management Dept, Shenzhen 518055, Peoples R China
3.Shenzhen Univ, Coll Comp Sci & Software, Shenzhen 518060, Peoples R China
4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

Chen, Renjie,Sun, Ning,Chen, Xiaojun,et al. Supervised Feature Selection With a Stratified Feature Weighting Method[J]. IEEE Access,2018,6:15087-15098.

Chen, Renjie,Sun, Ning,Chen, Xiaojun,Yang, Min,&Wu, Qingyao.(2018).Supervised Feature Selection With a Stratified Feature Weighting Method.IEEE Access,6,15087-15098.

Chen, Renjie,et al."Supervised Feature Selection With a Stratified Feature Weighting Method".IEEE Access 6(2018):15087-15098.