Variable Global Feature Selection Scheme for automatic classification of text documents
作者：
Highlights：
• A novel Variable Global Feature Selection Scheme (VGFSS) is proposed.
• VGFSS selects variable number of features from each class instead of equal features.
• The selection of features in VGFSS is based on distribution of terms in the classes.
• The methods are evaluated using Macro_F1 and Micro_F1 measure followed by Z-test.
• The VGFSS algorithm outperforms among seven competing methods in benchmark datasets.
摘要
•A novel Variable Global Feature Selection Scheme (VGFSS) is proposed.•VGFSS selects variable number of features from each class instead of equal features.•The selection of features in VGFSS is based on distribution of terms in the classes.•The methods are evaluated using Macro_F1 and Micro_F1 measure followed by Z-test.•The VGFSS algorithm outperforms among seven competing methods in benchmark datasets.
论文关键词：Feature selection,Text document classification,Text mining,Text analysis
论文评审过程：Received 6 January 2017, Revised 16 March 2017, Accepted 24 March 2017, Available online 27 March 2017, Version of Record 31 March 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.057
