location: Current position: Home>> Scientific Research>> Paper Publications

Maximum relevance minimum common redundancy feature selection for nonlinear data

Hits:

Affiliation of Author(s):数学与统计学院

Title of Paper:Maximum relevance minimum common redundancy feature selection for nonlinear data

Journal:Information Sciences

Indexed by:Article

Correspondence Author:JinXing Che,YouLong Yang, Li Li, XuYing Bai

Document Code:WOS: 000404202700006 EI:20172003671524

Document Type:J

Volume:409

Issue:10

Page Number:68-86

Translation or Not:no

Date of Publication:2017-10-01

Included Journals:SCI、EI

Pre One:Fuzzy rule-based oversampling technique for imbalanced and incomplete data learning

Next One:Stochastic correlation coefficient ensembles for variable selection

Baidu
map